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{"reason":"idle timeout","timestamp":1776932392614}
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31521
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<h2>新任务场景草图：双臂空中穿环插入</h2>
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<p class="subtitle">保持双 Diana + 同一张桌子；左侧放环状木块，右侧放长条木块，先各自抓起，再在空中完成穿孔插入。</p>
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<div class="split">
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<div class="mockup">
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<div class="mockup-header">俯视布局（初始）</div>
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<div class="mockup-body">
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<div style="position:relative;height:340px;background:#f3f4f6;border-radius:16px;border:1px solid #d1d5db;overflow:hidden;">
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<div style="position:absolute;left:12%;top:18%;width:22%;height:64%;border:2px dashed #60a5fa;border-radius:18px;background:rgba(96,165,250,.08);display:flex;align-items:flex-start;justify-content:center;padding-top:8px;font-weight:700;">左臂采样区（环）</div>
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<div style="position:absolute;right:12%;top:18%;width:22%;height:64%;border:2px dashed #f59e0b;border-radius:18px;background:rgba(245,158,11,.08);display:flex;align-items:flex-start;justify-content:center;padding-top:8px;font-weight:700;">右臂采样区（长条）</div>
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<div style="position:absolute;left:50%;top:50%;transform:translate(-50%,-50%);width:120px;height:120px;border:10px solid #ef4444;background:white;box-sizing:border-box;display:flex;align-items:center;justify-content:center;font-weight:700;color:#ef4444;">环块</div>
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<div style="position:absolute;left:72%;top:50%;transform:translate(-50%,-50%) rotate(0deg);width:110px;height:22px;background:#10b981;border:2px solid #065f46;border-radius:6px;display:flex;align-items:center;justify-content:center;color:white;font-size:12px;font-weight:700;">长条</div>
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<div style="position:absolute;left:50%;top:12%;transform:translateX(-50%);padding:6px 10px;background:#111827;color:white;border-radius:999px;font-size:12px;">桌面中心 / 空中插入区上方</div>
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</div>
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</div>
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</div>
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<div class="mockup">
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<div class="mockup-header">任务阶段</div>
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<div class="mockup-body">
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<div class="section">
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<div class="label">Phase 1</div>
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<h3>左右臂分别接近目标</h3>
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<p>左臂只抓环状木块，右臂只抓长条木块。两物体初始姿态固定，只随机平面位置。</p>
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</div>
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<div class="section">
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<div class="label">Phase 2</div>
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<h3>同时抓起并离桌</h3>
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<p>两个物体都需要离开桌面后，才允许触发最终成功奖励。</p>
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</div>
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<div class="section">
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<div class="label">Phase 3</div>
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<h3>空中对齐并插入</h3>
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<p>在桌面上方的会合区完成对齐，让长条沿自身长轴穿过环孔；一旦穿孔成功即判成功。</p>
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</div>
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</div>
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</div>
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</div>
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<div class="section">
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<h3>奖励草案（总分 5）</h3>
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<div class="cards">
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<div class="card"><div class="card-body"><h3>+1</h3><p>左臂碰到环块</p></div></div>
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<div class="card"><div class="card-body"><h3>+1</h3><p>右臂碰到长条</p></div></div>
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<div class="card"><div class="card-body"><h3>+1</h3><p>环块离桌</p></div></div>
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<div class="card"><div class="card-body"><h3>+1</h3><p>长条离桌</p></div></div>
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<div class="card"><div class="card-body"><h3>+1</h3><p>两者离桌时，长条穿过环孔</p></div></div>
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</div>
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</div>
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<div style="display:flex;align-items:center;justify-content:center;min-height:60vh"><p class="subtitle">Continuing in terminal...</p></div>
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# Repository Guidelines
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## Project Structure & Module Organization
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- `roboimi/` is the main package: simulation code in `roboimi/envs/`, assets/helpers in `roboimi/assets/`, VLA models/config in `roboimi/vla/`, and runnable scripts in `roboimi/demos/` plus `roboimi/demos/vla_scripts/`.
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- `tests/` holds `unittest` regressions for training, rollout, dataset loading, and asset resolution.
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- `docs/superpowers/` stores design specs and plans.
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- `docs/experiment-guide.md` store how beginning experiment
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- `runs/`, `outputs/`, `swanlog/`, `.worktrees/`, and `checkpoints/` are generated experiment artifacts, not source modules.
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## Build, Test, and Development Commands
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- `conda env create -f environment.yml && conda activate roboimi` — create the standard environment.
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- `pip install -e .` — editable install.
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- `python -m unittest discover -s tests -p 'test_*.py'` — run all tests.
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- `python -m unittest tests.test_train_vla_rollout_validation -v` — run one focused test module.
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- `python roboimi/vla/scripts/calculate_stats.py` — regenerate normalization stats.
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- `python roboimi/demos/vla_scripts/train_vla.py train.max_steps=1000` — quick training smoke test.
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- `python roboimi/demos/vla_scripts/eval_vla.py eval.ckpt_path=checkpoints/vla_model_best.pt eval.num_episodes=5` — evaluate a checkpoint.
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## Coding Style & Naming Conventions
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- Use Python, 4-space indentation, and PEP 8–style naming.
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- Prefer `snake_case` for files, functions, variables, and Hydra keys; use `CamelCase` for classes.
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- Match existing Hydra namespaces such as `train.*`, `eval.*`, and `agent.*`.
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- Keep diffs focused and imports tidy. Experiment names usually encode host/GPU, e.g. `...-l20g3-...`, `...-5880g1-...`, or `...-5090-...`.
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## Testing Guidelines
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- Tests use `unittest` and `unittest.mock`.
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- Name files `tests/test_*.py` and classes `*Test`.
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- Add direct regression coverage for changes to training, rollout, config, or dataset code paths.
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- No strict coverage target is defined, but changed behavior should be tested and affected suites should pass before review.
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## Commit & Pull Request Guidelines
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||||||
- Follow existing prefixes: `feat`, `fix`, `chore`, `docs`, `debug`, optionally with scope, e.g. `feat(eval): export rollout video timing`.
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- Keep commit messages imperative and specific.
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- PRs should state the goal, touched configs/scripts, exact test commands, and dataset/checkpoint assumptions. Attach visual artifacts only when outputs change.
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## Experiment Host Reference
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||||||
- **Local workstation (`droid-z790eagleax`)** — local shell; **1× RTX 5090 32 GB**; env: `/home/droid/.conda/envs/roboimi/bin/python`.
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||||||
- **5880 node (`droid-System-Product-Name`)** — username: `droid`; IP: `100.73.14.65`; connect with `ssh droid@100.73.14.65`; **2× RTX 5880 Ada 48 GB**; env: `/home/droid/miniforge3/envs/roboimi/bin/python`; trusted tailnet host.
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||||||
- **L20 node (`droid-G5500-V7`)** — username: `droid`; IP: `100.119.99.14`; connect with `ssh droid@100.119.99.14`; **8× NVIDIA L20 46 GB**; env: `/home/droid/miniforge3/envs/roboimi/bin/python`; store large experiment data under `/data`.
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"""Capture first and last (success) frames from front camera for both sim tasks."""
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import os
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import time
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import numpy as np
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import cv2
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from roboimi.envs.double_pos_ctrl_env import make_sim_env
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from roboimi.demos.diana_policy import TestPickAndTransferPolicy
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from roboimi.demos.diana_air_insert_policy import TestAirInsertPolicy
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from roboimi.utils.act_ex_utils import sample_transfer_pose, sample_air_insert_socket_peg_state
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OUTPUT_DIR = "sim_frames"
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IMG_SIZE = (256, 256)
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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def capture_task(task_name, policy, task_state, episode_len):
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env = make_sim_env(task_name, headless=True)
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env.reset(task_state)
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time.sleep(1)
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env._update_camera_images_sync()
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first_frame = cv2.resize(env.front, IMG_SIZE)
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last_success_frame = None
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max_reward = 0
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for step in range(episode_len):
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action = policy.predict(task_state, step)
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env.step(action)
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env._update_camera_images_sync()
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if env.rew is not None and env.rew >= max_reward:
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max_reward = env.rew
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if env.rew == env.max_reward:
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last_success_frame = cv2.resize(env.front, IMG_SIZE)
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if last_success_frame is None:
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last_success_frame = cv2.resize(env.front, IMG_SIZE)
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print(f" [WARN] {task_name}: max_reward={max_reward}/{env.max_reward}, using last frame")
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else:
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print(f" {task_name}: success! max_reward={max_reward}")
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return first_frame, last_success_frame
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def main():
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# Transfer task (episode_len=700, policy covers full range)
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print("Running sim_transfer...")
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task_state_transfer = sample_transfer_pose()
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policy_transfer = TestPickAndTransferPolicy(inject_noise=False)
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first_t, last_t = capture_task('sim_transfer', policy_transfer, task_state_transfer, 700)
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cv2.imwrite(os.path.join(OUTPUT_DIR, "transfer_front_first.png"), first_t)
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cv2.imwrite(os.path.join(OUTPUT_DIR, "transfer_front_last.png"), last_t)
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# Air insert / peg-in-socket (policy EPISODE_END_T=600)
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print("Running sim_air_insert_socket_peg...")
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task_state_insert = sample_air_insert_socket_peg_state()
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policy_insert = TestAirInsertPolicy(inject_noise=False)
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first_i, last_i = capture_task('sim_air_insert_socket_peg', policy_insert, task_state_insert, 600)
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cv2.imwrite(os.path.join(OUTPUT_DIR, "air_insert_front_first.png"), first_i)
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cv2.imwrite(os.path.join(OUTPUT_DIR, "air_insert_front_last.png"), last_i)
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print(f"\nDone! Images saved to {OUTPUT_DIR}/")
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if __name__ == '__main__':
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main()
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# 实验操作指南
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||||||
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||||||
## 0. 适用范围与当前起点
|
|
||||||
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|
||||||
这份文档面向 **当前 `main` 分支** 的通用训练 / 验证 / 评估流程，不绑定某一条历史实验分支。
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|
||||||
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||||||
当前默认 Hydra 起点：
|
|
||||||
- agent：`resnet_transformer`
|
|
||||||
- data：`simpe_robot_dataset`
|
|
||||||
- eval：`eval`
|
|
||||||
|
|
||||||
当前仓库里常见可选 agent 配置包括：
|
|
||||||
- `resnet_transformer`
|
|
||||||
- `resnet_diffusion`
|
|
||||||
- `resnet_gr00t_dit`
|
|
||||||
- `resnet_imf_attnres`
|
|
||||||
- `resnet_imf_attnres_multitoken`
|
|
||||||
- `siglip2_imf_attnres`
|
|
||||||
- `lewm_imf_attnres`
|
|
||||||
|
|
||||||
说明：
|
|
||||||
- `experiment_suites/` 下的历史目录仍然可以作为参考，但**正文以当前 `main` 的通用流程为准**。
|
|
||||||
- 如果你只是想快速开始，优先从默认 `resnet_transformer` 配置出发，再按实验需求替换 agent。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 1. 关键文件与入口
|
|
||||||
|
|
||||||
| 路径 | 作用 |
|
|
||||||
| --- | --- |
|
|
||||||
| `roboimi/demos/vla_scripts/train_vla.py` | 主训练入口；负责数据集、checkpoint、`val/loss`、held-out action MSE、训练期 rollout 验证、SwanLab |
|
|
||||||
| `roboimi/demos/vla_scripts/eval_vla.py` | 单次 rollout / 离线评估入口；支持 headless、summary、trajectory image、video artifact、多进程并行 rollout |
|
|
||||||
| `roboimi/vla/conf/config.yaml` | 全局 Hydra 训练配置 |
|
|
||||||
| `roboimi/vla/conf/data/simpe_robot_dataset.yaml` | 默认数据集配置；数据路径、相机名、图像 resize 都在这里 |
|
|
||||||
| `roboimi/vla/conf/eval/eval.yaml` | eval 默认配置；`eval.ckpt_path`、`eval.num_episodes`、artifact 开关、多进程 rollout 配置都在这里 |
|
|
||||||
| `roboimi/vla/conf/agent/*.yaml` | 可选 agent 配置集合 |
|
|
||||||
| `roboimi/vla/data/simpe_robot_dataset.py` | HDF5 懒加载数据集；支持 `episode_indices` 过滤与 `available_episode_indices` 元信息 |
|
|
||||||
| `roboimi/vla/scripts/calculate_stats.py` | 重算 `dataset_stats.pkl` |
|
|
||||||
| `experiment_suites/` | 历史实验目录；可参考，但不作为当前主流程的唯一事实来源 |
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 2. 三台机器与环境
|
|
||||||
|
|
||||||
| 机器 | GPU | Python | 常用数据集路径 | 备注 |
|
|
||||||
| --- | --- | --- | --- | --- |
|
|
||||||
| 本地 `droid-z790eagleax` | 1× RTX 5090 32GB | `/home/droid/.conda/envs/roboimi/bin/python` | `/home/droid/project/diana_sim/sim_transfer` | 适合 smoke、单条主跑、本地调参 |
|
|
||||||
| 5880 节点 `100.73.14.65` | 2× RTX 5880 Ada 48GB | `/home/droid/miniforge3/envs/roboimi/bin/python` | `/home/droid/sim_dataset/sim_transfer` | 适合 2 条并行主跑 |
|
|
||||||
| L20 节点 `100.119.99.14` | 8× NVIDIA L20 46GB | `/home/droid/miniforge3/envs/roboimi/bin/python` | `/data/simtransfer/current` | 适合大规模 grid；建议数据与 run 放 `/data` |
|
|
||||||
|
|
||||||
连接：
|
|
||||||
- 5880：`ssh droid@100.73.14.65`
|
|
||||||
- L20：`ssh droid@100.119.99.14`
|
|
||||||
|
|
||||||
说明：
|
|
||||||
- repo / worktree 路径可能会随当前 checkout 或同步目录变化，**以你当前机器上的实际 repo 路径为准**。
|
|
||||||
- 如果训练目录会被 Hydra 自动切走，建议显式设置 `hydra.run.dir=...` 以固定输出目录。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 3. 训练流怎么走
|
|
||||||
|
|
||||||
`train_vla.py` 当前主流程如下：
|
|
||||||
|
|
||||||
1. 读取 Hydra 配置并打印完整 cfg
|
|
||||||
2. 根据配置确定数据集图像 resize：
|
|
||||||
- 默认用 `data.image_resize_shape`
|
|
||||||
- 如果 `agent.vision_backbone.dataset_image_resize_shape` 存在，则优先用 backbone 覆盖值
|
|
||||||
3. 通过 `build_train_val_datasets()` 构建 dataset / train dataset / val dataset
|
|
||||||
- 若设置 `train.val_episode_indices`：按显式 episode 切出 held-out val
|
|
||||||
- 否则按 `train.val_split` 做随机划分
|
|
||||||
4. 用 `DataLoader` 建 train / val loader
|
|
||||||
5. 从 `dataset_dir/dataset_stats.pkl` 读取归一化统计
|
|
||||||
6. 实例化当前指定的 agent：`instantiate(cfg.agent, dataset_stats=...)`
|
|
||||||
7. 可选加载：
|
|
||||||
- `train.pretrained_ckpt`：微调起点
|
|
||||||
- `train.resume_ckpt`：断点续训（支持显式路径或 `auto`）
|
|
||||||
8. 建 optimizer 与 scheduler
|
|
||||||
9. 训练循环里按 `log_freq` 记录 train loss / lr
|
|
||||||
10. 按 `save_freq` 保存 `checkpoints/vla_model_step_*.pt`，并在有 val loader 时计算 `val/loss`
|
|
||||||
11. 若设置了显式 held-out val，并且 `train.action_mse_val_freq_epochs > 0`，则按 epoch 计算 held-out `action MSE`
|
|
||||||
12. 按 `train.rollout_val_freq_epochs` 跑训练期 rollout 验证
|
|
||||||
13. 最后写：
|
|
||||||
- `checkpoints/vla_model_best.pt`
|
|
||||||
- `checkpoints/vla_model_final.pt`
|
|
||||||
|
|
||||||
当前 best model 选择逻辑：
|
|
||||||
- **第一次拿到 rollout reward 之前**：先用 `val_loss`（或 train loss 回退）挑 best
|
|
||||||
- **第一次 rollout 之后**：优先用 `rollout_avg_reward` 挑 best
|
|
||||||
|
|
||||||
输出目录：
|
|
||||||
- 当前代码实际输出写到 **Hydra runtime output dir** 下
|
|
||||||
- checkpoint 默认在：`<hydra_output_dir>/checkpoints/`
|
|
||||||
- 如果不显式指定 `hydra.run.dir`，Hydra 会自动生成目录
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 4. 验证流怎么走
|
|
||||||
|
|
||||||
### 4.1 常规 `val/loss`
|
|
||||||
|
|
||||||
常规验证有两种来源：
|
|
||||||
|
|
||||||
1. **随机划分验证集**
|
|
||||||
- 用 `train.val_split > 0`
|
|
||||||
- 训练脚本会在保存 checkpoint 时计算 `val/loss`
|
|
||||||
|
|
||||||
2. **显式 held-out episode 验证集**
|
|
||||||
- 用 `train.val_episode_indices=[...]`
|
|
||||||
- 训练集 = 全部 episode - held-out episode
|
|
||||||
- 验证集 = held-out episode
|
|
||||||
- checkpoint 保存时同样会计算 `val/loss`
|
|
||||||
|
|
||||||
说明：
|
|
||||||
- 若 `train.val_split=0.0` 且未设置 `train.val_episode_indices`，则不会有 `val/loss`。
|
|
||||||
|
|
||||||
### 4.2 held-out action MSE
|
|
||||||
|
|
||||||
如果想对固定 held-out episode 做更稳定、可复现的动作预测误差验证，推荐使用：
|
|
||||||
|
|
||||||
- `train.val_split=0.0`
|
|
||||||
- `train.val_episode_indices=[100]`（或你指定的 episode 列表）
|
|
||||||
- `train.action_mse_val_freq_epochs=1`
|
|
||||||
|
|
||||||
当前 `main` 中这套逻辑会：
|
|
||||||
- 每隔 `action_mse_val_freq_epochs` 个 epoch
|
|
||||||
- 对 held-out val loader 调 `agent.predict_action_chunk(...)`
|
|
||||||
- 与 batch 中的 `action` 计算 masked MSE
|
|
||||||
- 如果存在 `action_is_pad`，会自动 mask 掉 padding 部分
|
|
||||||
|
|
||||||
日志 key：
|
|
||||||
- 控制台 / `train_vla.log`：`held-out action MSE`
|
|
||||||
- SwanLab：`val/action_mse`
|
|
||||||
|
|
||||||
重要约束：
|
|
||||||
- `train.action_mse_val_freq_epochs > 0` **必须搭配** `train.val_episode_indices`
|
|
||||||
- 如果只设了 `action_mse_val_freq_epochs` 而没有设 `val_episode_indices`，训练会直接报错
|
|
||||||
|
|
||||||
### 4.3 rollout 验证
|
|
||||||
|
|
||||||
训练内 rollout 验证由：
|
|
||||||
- `train_vla.py -> run_rollout_validation() -> eval_vla._run_eval()`
|
|
||||||
|
|
||||||
当前训练内 rollout 会强制：
|
|
||||||
- `headless=true`
|
|
||||||
- `verbose_action=false`
|
|
||||||
- `record_video=false`
|
|
||||||
- `save_trajectory_image=true`
|
|
||||||
- `trajectory_image_camera_name=front`
|
|
||||||
- `save_summary_json=true`
|
|
||||||
|
|
||||||
当前这套路径是**配置驱动**的：
|
|
||||||
- `train.rollout_device`：默认跟随 `train.device`
|
|
||||||
- `train.rollout_num_workers`：默认 `null`
|
|
||||||
- 当 rollout 设备是 CPU 时，自动退化为 `1`
|
|
||||||
- 当 rollout 设备是 CUDA 时，自动推断为 `min(train.rollout_num_episodes, 8)`
|
|
||||||
- `train.rollout_cuda_devices`：默认 `null`，等价于逻辑 GPU `[0]`
|
|
||||||
- `train.rollout_response_timeout_s`
|
|
||||||
- `train.rollout_server_startup_timeout_s`
|
|
||||||
|
|
||||||
所以现在：
|
|
||||||
- 训练在 CUDA 上时，训练期 rollout 默认也会走 GPU
|
|
||||||
- `rollout_num_workers > 1` 时，会走并行 rollout
|
|
||||||
- 可以是 **单 GPU 多 worker 共用一个 inference server**
|
|
||||||
- 也可以是 **多 GPU 多 server 分摊 worker**
|
|
||||||
|
|
||||||
训练内 rollout artifact 默认落到：
|
|
||||||
- `<hydra_output_dir>/rollout_artifacts/<checkpoint_stem>/`
|
|
||||||
|
|
||||||
常见文件：
|
|
||||||
- `rollout_summary.json`
|
|
||||||
- `rollout_front_ep01_trajectory.png` ...
|
|
||||||
|
|
||||||
日志重点看：
|
|
||||||
- `Epoch X rollout 平均奖励`
|
|
||||||
- `最佳模型已更新`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 5. 数据集加载与 `val_episode_indices` 机制
|
|
||||||
|
|
||||||
### 5.1 数据集格式
|
|
||||||
|
|
||||||
`SimpleRobotDataset` 读取 `dataset_dir` 下的 `*.hdf5` / `episode_*.hdf5`，每个 episode 文件至少要有：
|
|
||||||
- `action`
|
|
||||||
- `observations/qpos`
|
|
||||||
- `observations/images/{cam_name}`
|
|
||||||
|
|
||||||
默认数据配置中的相机：
|
|
||||||
- `r_vis`
|
|
||||||
- `top`
|
|
||||||
- `front`
|
|
||||||
|
|
||||||
### 5.2 懒加载行为
|
|
||||||
|
|
||||||
`roboimi/vla/data/simpe_robot_dataset.py` 是按帧懒加载，不会一次性把整套 HDF5 全读进内存。
|
|
||||||
|
|
||||||
它会：
|
|
||||||
- 扫描目录下的 HDF5 文件
|
|
||||||
- 在 worker 内做 HDF5 文件句柄 LRU 缓存
|
|
||||||
- 根据文件名中的 `episode_XXX` 建立 `available_episode_indices`
|
|
||||||
|
|
||||||
### 5.3 `val_episode_indices` 怎么切
|
|
||||||
|
|
||||||
`build_train_val_datasets()` 的逻辑是：
|
|
||||||
|
|
||||||
1. 先 instantiate 一次完整 dataset
|
|
||||||
2. 读取 `dataset.available_episode_indices`
|
|
||||||
3. 检查 `train.val_episode_indices` 是否都存在
|
|
||||||
4. 用 `episode_indices=` 再各 instantiate 一次：
|
|
||||||
- train dataset = 全部 episode - held-out episode
|
|
||||||
- val dataset = 只包含 held-out episode
|
|
||||||
|
|
||||||
因此：
|
|
||||||
- `train.val_episode_indices=[100]` 的意思是把 `episode_100.hdf5` 整个拿去做 held-out val
|
|
||||||
- 如果 episode 不存在，会直接报错
|
|
||||||
- 如果你把所有 episode 都塞进 `val_episode_indices`，也会直接报错，因为训练集会变空
|
|
||||||
|
|
||||||
### 5.4 图像 resize 与返回字段
|
|
||||||
|
|
||||||
dataset 侧 resize 默认来自：
|
|
||||||
- `data.image_resize_shape`
|
|
||||||
- 如果 backbone 额外覆盖，则优先 `agent.vision_backbone.dataset_image_resize_shape`
|
|
||||||
|
|
||||||
当前通用 batch 返回字段包括：
|
|
||||||
- `observation.state`
|
|
||||||
- `observation_is_pad`
|
|
||||||
- `observation.<cam>`
|
|
||||||
- `action`
|
|
||||||
- `action_is_pad`
|
|
||||||
- `task`
|
|
||||||
|
|
||||||
### 5.5 统计文件
|
|
||||||
|
|
||||||
训练和推理都默认依赖 `dataset_stats.pkl`。数据集更新后需要重算：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
|
|
||||||
--dataset_dir /home/droid/project/diana_sim/sim_transfer
|
|
||||||
```
|
|
||||||
|
|
||||||
远端只要把：
|
|
||||||
- Python 路径
|
|
||||||
- `--dataset_dir`
|
|
||||||
|
|
||||||
替换成对应机器上的实际路径即可。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 6. SwanLab 行为
|
|
||||||
|
|
||||||
当前默认配置里：
|
|
||||||
- `train.use_swanlab=false`
|
|
||||||
|
|
||||||
如果要开启，通常显式设置：
|
|
||||||
- `train.use_swanlab=true`
|
|
||||||
- `train.swanlab_project=roboimi-vla`
|
|
||||||
- `train.swanlab_run_name=<run_name>`
|
|
||||||
|
|
||||||
`train_vla.py` 当前会记录：
|
|
||||||
- 初始化时上传 `train` / `data` / `agent` 三段 config
|
|
||||||
- 训练中：
|
|
||||||
- `train/loss`
|
|
||||||
- `train/lr`
|
|
||||||
- `train/best_loss`
|
|
||||||
- `train/step`
|
|
||||||
- checkpoint 验证时：
|
|
||||||
- `val/loss`
|
|
||||||
- held-out 数值验证时：
|
|
||||||
- `val/action_mse`
|
|
||||||
- rollout 验证时：
|
|
||||||
- `rollout/avg_reward`
|
|
||||||
- `rollout/epoch`
|
|
||||||
- 训练结束时：
|
|
||||||
- `final/checkpoint_path`
|
|
||||||
- `final/best_checkpoint_path`
|
|
||||||
|
|
||||||
训练期 rollout 生成的前视图轨迹 PNG 会 best-effort 上传到 SwanLab；上传失败只会 warning，不会让训练中断。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 7. 并行 rollout 说明
|
|
||||||
|
|
||||||
### 7.1 这套能力现在在哪里
|
|
||||||
|
|
||||||
当前主仓库已经**内置**多进程并行 rollout 能力，入口就是：
|
|
||||||
- `roboimi/demos/vla_scripts/eval_vla.py`
|
|
||||||
|
|
||||||
控制参数：
|
|
||||||
- `eval.num_workers`
|
|
||||||
- `eval.cuda_devices`
|
|
||||||
|
|
||||||
语义：
|
|
||||||
- `eval.num_workers`：环境 worker 数，按 episode 切分
|
|
||||||
- `eval.cuda_devices`：推理 server 绑定到哪些逻辑 GPU
|
|
||||||
|
|
||||||
### 7.2 两种常见模式
|
|
||||||
|
|
||||||
1. **单机单卡，多 worker 共用同一张 GPU**
|
|
||||||
- 典型：5090 只有 1 卡，但想让 4 个 rollout worker 并行跑环境
|
|
||||||
- 形式：`eval.device=cuda eval.num_workers=4 'eval.cuda_devices=[0]'`
|
|
||||||
- 这时是 **1 个 CUDA inference server + 4 个 env worker**
|
|
||||||
|
|
||||||
2. **单机多卡，多 server 分摊 worker**
|
|
||||||
- 典型：5880 / L20 有多卡
|
|
||||||
- 形式：`eval.device=cuda eval.num_workers=8 'eval.cuda_devices=[0,1]'`
|
|
||||||
- worker 会按 round-robin 分到多个 server 上
|
|
||||||
|
|
||||||
### 7.3 操作约束
|
|
||||||
|
|
||||||
- 并行 rollout 依赖 **多进程 eval 路径**，不是 `train.num_workers`
|
|
||||||
- `train.num_workers` 是 DataLoader worker，和 rollout 并行不是一回事
|
|
||||||
- `eval.num_workers > 1` 时必须 `eval.headless=true`
|
|
||||||
- worker 数会自动 cap 到 `eval.num_episodes`
|
|
||||||
- 多 worker 时**不支持**同时导出：
|
|
||||||
- `eval.record_video=true`
|
|
||||||
- `eval.save_trajectory=true`
|
|
||||||
- `eval.save_trajectory_npz=true`
|
|
||||||
- `eval.save_trajectory_image=true` 可以开，适合并行 reward + 定性检查一起做
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 8. 常用命令模板
|
|
||||||
|
|
||||||
下面的模板都以 **当前 `main` 通用流程** 为准。远端机器只需要替换：
|
|
||||||
- Python 路径
|
|
||||||
- repo 路径
|
|
||||||
- `data.dataset_dir`
|
|
||||||
- `hydra.run.dir`
|
|
||||||
|
|
||||||
### 8.1 本地 smoke train
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda \
|
|
||||||
train.batch_size=8 \
|
|
||||||
train.max_steps=1000 \
|
|
||||||
train.num_workers=4 \
|
|
||||||
train.save_freq=200 \
|
|
||||||
hydra.run.dir=/home/droid/project/roboimi/runs/smoke-resnet-transformer
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.2 通用训练模板
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda \
|
|
||||||
train.batch_size=16 \
|
|
||||||
train.lr=0.0001 \
|
|
||||||
train.max_steps=100000 \
|
|
||||||
train.num_workers=4 \
|
|
||||||
train.save_freq=2000 \
|
|
||||||
train.use_swanlab=true \
|
|
||||||
train.swanlab_project=roboimi-vla \
|
|
||||||
train.swanlab_run_name=<run_name> \
|
|
||||||
hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.3 带 held-out episode 验证的训练模板
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda \
|
|
||||||
train.batch_size=16 \
|
|
||||||
train.lr=0.0001 \
|
|
||||||
train.max_steps=100000 \
|
|
||||||
train.num_workers=4 \
|
|
||||||
train.val_split=0.0 \
|
|
||||||
'train.val_episode_indices=[100]' \
|
|
||||||
train.action_mse_val_freq_epochs=1 \
|
|
||||||
train.rollout_val_freq_epochs=5 \
|
|
||||||
train.rollout_num_episodes=10 \
|
|
||||||
train.use_swanlab=true \
|
|
||||||
train.swanlab_project=roboimi-vla \
|
|
||||||
train.swanlab_run_name=<run_name> \
|
|
||||||
hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.4 断点续训模板
|
|
||||||
|
|
||||||
`train_vla.py` 支持通过 CLI override 使用 `train.resume_ckpt`：
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda \
|
|
||||||
train.resume_ckpt=auto \
|
|
||||||
hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
|
|
||||||
```
|
|
||||||
|
|
||||||
也可以把 `train.resume_ckpt` 换成显式 checkpoint 路径。
|
|
||||||
|
|
||||||
### 8.5 单次离线评估
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda eval.device=cuda \
|
|
||||||
eval.ckpt_path=/home/droid/project/roboimi/runs/<run_name>/checkpoints/vla_model_best.pt \
|
|
||||||
eval.num_episodes=10 \
|
|
||||||
eval.headless=true \
|
|
||||||
eval.verbose_action=false \
|
|
||||||
eval.save_summary_json=true \
|
|
||||||
eval.save_trajectory_image=true \
|
|
||||||
eval.trajectory_image_camera_name=front \
|
|
||||||
eval.artifact_dir=/tmp/roboimi_eval_front
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.6 并行离线评估
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda eval.device=cuda \
|
|
||||||
eval.ckpt_path=/home/droid/project/roboimi/runs/<run_name>/checkpoints/vla_model_best.pt \
|
|
||||||
eval.num_episodes=10 \
|
|
||||||
eval.num_workers=4 \
|
|
||||||
'eval.cuda_devices=[0]' \
|
|
||||||
eval.headless=true \
|
|
||||||
eval.verbose_action=false \
|
|
||||||
eval.save_summary_json=true \
|
|
||||||
eval.save_trajectory_image=true \
|
|
||||||
eval.trajectory_image_camera_name=front \
|
|
||||||
eval.artifact_dir=/tmp/roboimi_parallel_eval
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.7 训练内启用并行 GPU rollout
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
agent=resnet_transformer \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.device=cuda \
|
|
||||||
train.batch_size=16 \
|
|
||||||
train.lr=0.0001 \
|
|
||||||
train.max_steps=100000 \
|
|
||||||
train.num_workers=4 \
|
|
||||||
train.rollout_val_freq_epochs=5 \
|
|
||||||
train.rollout_num_episodes=10 \
|
|
||||||
train.rollout_device=cuda \
|
|
||||||
train.rollout_num_workers=4 \
|
|
||||||
'train.rollout_cuda_devices=[0]' \
|
|
||||||
train.rollout_validate_on_checkpoint=false \
|
|
||||||
train.use_swanlab=true \
|
|
||||||
train.swanlab_project=roboimi-vla \
|
|
||||||
train.swanlab_run_name=<run_name> \
|
|
||||||
hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.8 重算统计文件
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
|
|
||||||
--dataset_dir /home/droid/project/diana_sim/sim_transfer
|
|
||||||
```
|
|
||||||
|
|
||||||
### 8.9 监控日志
|
|
||||||
|
|
||||||
```bash
|
|
||||||
tail -f runs/<run_name>/train_vla.log
|
|
||||||
```
|
|
||||||
|
|
||||||
如果你显式设置了 `hydra.run.dir`，优先直接 tail 对应绝对路径下的日志文件。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## 9. 操作建议
|
|
||||||
|
|
||||||
- **优先固定 `hydra.run.dir`**，避免输出目录分散在 Hydra 自动生成路径里
|
|
||||||
- 如果只需要常规验证，使用：
|
|
||||||
- `train.val_split > 0`
|
|
||||||
- 如果需要稳定、可复现的 held-out 指标，使用：
|
|
||||||
- `train.val_split=0.0`
|
|
||||||
- `train.val_episode_indices=[...]`
|
|
||||||
- `train.action_mse_val_freq_epochs=1`
|
|
||||||
- `train.num_workers` 是 DataLoader worker，不等于 rollout 并行度
|
|
||||||
- rollout 并行优先看：
|
|
||||||
- `train.rollout_num_workers`
|
|
||||||
- `train.rollout_cuda_devices`
|
|
||||||
- `eval.num_workers`
|
|
||||||
- `eval.cuda_devices`
|
|
||||||
- 数据集更新后记得重算 `dataset_stats.pkl`
|
|
||||||
- 若同时设置了：
|
|
||||||
- `train.pretrained_ckpt`
|
|
||||||
- `train.resume_ckpt`
|
|
||||||
当前逻辑会优先走 `resume_ckpt`
|
|
||||||
- 建议 run name 中带上关键信息：
|
|
||||||
- agent / horizon / batch size / lr / host / gpu / date
|
|
||||||
- `experiment_suites/` 下的历史目录适合作为参考资料，但不要默认把某一条旧 suite 当作当前主流程的唯一标准
|
|
||||||
@@ -0,0 +1,471 @@
|
|||||||
|
# feat-lewm-imf-fusion 实验操作指南
|
||||||
|
|
||||||
|
适用 worktree：`/home/droid/project/roboimi/.worktrees/feat-lewm-imf-fusion`
|
||||||
|
|
||||||
|
## 0. 先记住当前常用 recipe
|
||||||
|
|
||||||
|
当前这条分支最常用的训练/验证配方，直接参考：
|
||||||
|
`experiment_suites/2026-04-21-lewm-fromscratch-old9-epoch50-roll5-val-20260421-153037/`
|
||||||
|
|
||||||
|
核心约定：
|
||||||
|
- agent：`lewm_resnet_query_imf_attnres`
|
||||||
|
- from scratch：`train.pretrained_ckpt=null`，`agent.lewm_pretrained_ckpt=null`
|
||||||
|
- 训练：`batch_size=32`，`lr=1e-4`，`max_steps=109350`，`save_freq=10000`
|
||||||
|
- 数值验证：`train.val_split=0.0` + `train.val_episode_indices=[100]`
|
||||||
|
- held-out numeric validation：`train.action_mse_val_freq_epochs=1`
|
||||||
|
- rollout validation：`train.rollout_val_freq_epochs=5`，`train.rollout_num_episodes=10`
|
||||||
|
- SwanLab：`train.use_swanlab=true`，project=`roboimi-vla`
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 1. 分支结构与关键文件
|
||||||
|
|
||||||
|
| 路径 | 作用 |
|
||||||
|
| --- | --- |
|
||||||
|
| `roboimi/demos/vla_scripts/train_vla.py` | 主训练入口；负责数据集、checkpoint、数值验证、训练期 rollout 验证、SwanLab |
|
||||||
|
| `roboimi/demos/vla_scripts/eval_vla.py` | 单次 rollout / 离线验证入口；支持 headless、summary、trajectory image/video artifact |
|
||||||
|
| `roboimi/vla/conf/config.yaml` | 全局 Hydra 配置；训练默认值都在这里 |
|
||||||
|
| `roboimi/vla/conf/eval/eval.yaml` | eval 默认配置；`eval.ckpt_path`、`eval.num_episodes`、artifact 开关都在这里 |
|
||||||
|
| `roboimi/vla/conf/agent/lewm_resnet_query_imf_attnres.yaml` | 本分支最常用 agent；LeWM query fusion + IMF AttnRes head |
|
||||||
|
| `roboimi/vla/conf/backbone/lewm_resnet_query_fusion.yaml` | LeWM 多视角 ResNet query fusion backbone 配置 |
|
||||||
|
| `roboimi/vla/agent_imf.py` | `IMFVLAAgent` 实现；one-step IMF 推理、LeWM loss、LeWM 预训练组件加载 |
|
||||||
|
| `roboimi/vla/data/simpe_robot_dataset.py` | HDF5 懒加载数据集；也负责 `episode_indices` 过滤 |
|
||||||
|
| `roboimi/vla/scripts/calculate_stats.py` | 重算 `dataset_stats.pkl` |
|
||||||
|
| `experiment_suites/2026-04-21-lewm-fromscratch-old9-epoch50-roll5-val-20260421-153037/` | 当前最常用 suite；manifest、notes、launch log、local 启动脚本都在这里 |
|
||||||
|
|
||||||
|
补充：
|
||||||
|
- 本分支常用 run name 形如 `lewmimf-q08-ph08-ex08-emb384-l12-fromscratch-epoch50-step109350-5090g0-20260421-153037`
|
||||||
|
- `q08/ph16/ex08` 这类后缀分别对应 `agent.lewm_query_offsets`、`agent.pred_horizon`、`agent.num_action_steps`
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 2. 三台机器与环境
|
||||||
|
|
||||||
|
| 机器 | GPU | repo / worktree | Python | 常用数据集路径 |
|
||||||
|
| --- | --- | --- | --- | --- |
|
||||||
|
| 本地 `droid-z790eagleax` | 1× RTX 5090 32GB | `/home/droid/project/roboimi/.worktrees/feat-lewm-imf-fusion` | `/home/droid/.conda/envs/roboimi/bin/python` | `/home/droid/project/diana_sim/sim_transfer` |
|
||||||
|
| 5880 节点 `100.73.14.65` | 2× RTX 5880 Ada 48GB | `/home/droid/roboimi_suite_20260416_lewm_imf_fusion` | `/home/droid/miniforge3/envs/roboimi/bin/python` | `/home/droid/sim_dataset/sim_transfer` |
|
||||||
|
| L20 节点 `100.119.99.14` | 8× NVIDIA L20 46GB | `/data/roboimi_suite_20260416_lewm_imf_fusion` | `/home/droid/miniforge3/envs/roboimi/bin/python` | `/data/simtransfer/current` |
|
||||||
|
|
||||||
|
连接：
|
||||||
|
- 5880：`ssh droid@100.73.14.65`
|
||||||
|
- L20：`ssh droid@100.119.99.14`
|
||||||
|
|
||||||
|
经验规则：
|
||||||
|
- 本地 5090：适合单条 smoke / 小规模主跑 / 本地调参
|
||||||
|
- 5880：适合 2 条并行主跑
|
||||||
|
- L20：适合大 grid；数据和 run 建议都放 `/data`
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 3. 训练流怎么走
|
||||||
|
|
||||||
|
`train_vla.py` 的实际流程：
|
||||||
|
|
||||||
|
1. 读取 Hydra 配置并打印完整 cfg
|
||||||
|
2. 通过 `build_train_val_datasets()` 构建 train/val dataset
|
||||||
|
3. 用 `DataLoader` 建 train/val loader
|
||||||
|
4. 从 `dataset_dir/dataset_stats.pkl` 读取归一化统计
|
||||||
|
5. instantiate `IMFVLAAgent`
|
||||||
|
6. 可选加载：
|
||||||
|
- `train.pretrained_ckpt`
|
||||||
|
- `train.resume_ckpt`
|
||||||
|
- `agent.lewm_pretrained_ckpt`
|
||||||
|
7. 训练循环里按 `log_freq` 打 train loss / lr
|
||||||
|
8. 按 `save_freq` 保存 `checkpoints/vla_model_step_*.pt`
|
||||||
|
9. 每个 epoch 结束时，按配置跑：
|
||||||
|
- held-out action MSE
|
||||||
|
- rollout validation
|
||||||
|
10. 最后写：
|
||||||
|
- `checkpoints/vla_model_best.pt`
|
||||||
|
- `checkpoints/vla_model_final.pt`
|
||||||
|
|
||||||
|
当前 best model 选择逻辑：
|
||||||
|
- **第一次拿到 rollout reward 之前**：先用 `val_loss`（或 train loss 回退）挑 best
|
||||||
|
- **第一次 rollout 之后**：优先用 `rollout_avg_reward` 挑 best
|
||||||
|
|
||||||
|
输出目录一般通过 `hydra.run.dir=...` 固定；否则 Hydra 自己生成。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 4. 验证流怎么走
|
||||||
|
|
||||||
|
### 4.1 held-out 数值验证
|
||||||
|
|
||||||
|
当前常用做法不是随机切 `val_split`，而是：
|
||||||
|
- `train.val_split=0.0`
|
||||||
|
- `train.val_episode_indices=[100]`
|
||||||
|
- `train.action_mse_val_freq_epochs=1`
|
||||||
|
|
||||||
|
这样每个 epoch 结束都会在 `episode_100.hdf5` 上跑一次 `compute_action_mse_validation()`，日志 key 是：
|
||||||
|
- 控制台 / `train_vla.log`：`held-out action MSE`
|
||||||
|
- SwanLab：`val/action_mse`
|
||||||
|
|
||||||
|
### 4.2 rollout 验证
|
||||||
|
|
||||||
|
当前训练内 rollout 验证由 `train_vla.py -> run_rollout_validation() -> eval_vla._run_eval()` 触发。
|
||||||
|
|
||||||
|
当前这条分支的常用训练内 rollout 约束是：
|
||||||
|
- `train.rollout_val_freq_epochs=5`
|
||||||
|
- `train.rollout_num_episodes=10`
|
||||||
|
- `train.rollout_validate_on_checkpoint=false`
|
||||||
|
- 强制 headless
|
||||||
|
- 强制 `verbose_action=false`
|
||||||
|
- 强制 `record_video=false`
|
||||||
|
- 强制 `save_trajectory_image=true`
|
||||||
|
- 强制 `trajectory_image_camera_name=front`
|
||||||
|
- 强制 `save_summary_json=true`
|
||||||
|
|
||||||
|
当前已经修正为**配置驱动的 rollout device / worker 路径**：
|
||||||
|
- `train.rollout_device`：默认跟随 `train.device`
|
||||||
|
- `train.rollout_num_workers`：默认 `null`
|
||||||
|
- 当 rollout 设备是 CPU 时，自动退化为 `1`
|
||||||
|
- 当 rollout 设备是 CUDA 时，自动推断为 `min(train.rollout_num_episodes, 8)`
|
||||||
|
- `train.rollout_cuda_devices`：默认 `null`，等价于当前可见逻辑 GPU `[0]`
|
||||||
|
- `train.rollout_response_timeout_s`
|
||||||
|
- `train.rollout_server_startup_timeout_s`
|
||||||
|
|
||||||
|
所以现在：
|
||||||
|
- 训练在 `cuda` 上时，**训练期 rollout 默认会走 GPU**
|
||||||
|
- 如果 `rollout_num_workers > 1`，就会自动走并行 rollout
|
||||||
|
- 可以是 **单 GPU 多 worker 共用一个 inference server**
|
||||||
|
- 也可以是 **多 GPU 多 server 分摊 worker**
|
||||||
|
|
||||||
|
训练内 rollout artifact 默认落到：
|
||||||
|
`<hydra.run.dir>/rollout_artifacts/<checkpoint_stem>/`
|
||||||
|
|
||||||
|
常见文件：
|
||||||
|
- `rollout_summary.json`
|
||||||
|
- `rollout_front_ep01_trajectory.png` ... `rollout_front_ep10_trajectory.png`
|
||||||
|
|
||||||
|
日志重点看：
|
||||||
|
- `Epoch X rollout 平均奖励`
|
||||||
|
- `最佳模型已更新`
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 5. 数据集加载与 `val_episode_indices` 机制
|
||||||
|
|
||||||
|
### 5.1 数据集格式
|
||||||
|
|
||||||
|
`SimpleRobotDataset` 读取 `dataset_dir` 下的 `episode_*.hdf5`，每个 episode 文件里至少要有：
|
||||||
|
- `action`
|
||||||
|
- `observations/qpos`
|
||||||
|
- `observations/images/{cam_name}`
|
||||||
|
|
||||||
|
当前常用相机：
|
||||||
|
- `r_vis`
|
||||||
|
- `top`
|
||||||
|
- `front`
|
||||||
|
|
||||||
|
### 5.2 懒加载行为
|
||||||
|
|
||||||
|
`roboimi/vla/data/simpe_robot_dataset.py` 是按帧懒加载，不会一次性把整套 HDF5 全读进内存。
|
||||||
|
|
||||||
|
它会：
|
||||||
|
- 扫描目录下的 HDF5 文件
|
||||||
|
- 用文件名里的 episode 编号（如 `episode_100.hdf5` -> `100`）建立 `available_episode_indices`
|
||||||
|
- 在 worker 内做 HDF5 文件句柄 LRU 缓存
|
||||||
|
|
||||||
|
### 5.3 `val_episode_indices` 怎么切
|
||||||
|
|
||||||
|
`build_train_val_datasets()` 的逻辑是：
|
||||||
|
|
||||||
|
1. 先 instantiate 一次完整 dataset
|
||||||
|
2. 读取 `dataset.available_episode_indices`
|
||||||
|
3. 检查 `train.val_episode_indices` 是否都存在
|
||||||
|
4. 用 `episode_indices=` 再各 instantiate 一次：
|
||||||
|
- train dataset = 全部 episode - held-out episode
|
||||||
|
- val dataset = 只包含 held-out episode
|
||||||
|
|
||||||
|
因此：
|
||||||
|
- `train.val_episode_indices=[100]` 的意思是“把 `episode_100.hdf5` 整个拿去做 held-out val”
|
||||||
|
- 如果 episode 不存在，会直接报错
|
||||||
|
- 如果你把所有 episode 都塞进 `val_episode_indices`，也会直接报错，因为训练集会变空
|
||||||
|
|
||||||
|
### 5.4 图像 resize 与 LeWM 附加字段
|
||||||
|
|
||||||
|
dataset 侧 resize 默认来自：
|
||||||
|
- `data.image_resize_shape`
|
||||||
|
- 如果 backbone 额外覆盖，则优先 `agent.vision_backbone.dataset_image_resize_shape`
|
||||||
|
|
||||||
|
返回 batch 除了常规：
|
||||||
|
- `observation.state`
|
||||||
|
- `observation.<cam>`
|
||||||
|
- `action`
|
||||||
|
|
||||||
|
还会在 LeWM 打开时返回：
|
||||||
|
- `lewm.observation.state`
|
||||||
|
- `lewm.observation.<cam>`
|
||||||
|
- `lewm.future.state`
|
||||||
|
- `lewm.future.<cam>`
|
||||||
|
|
||||||
|
### 5.5 统计文件
|
||||||
|
|
||||||
|
训练和推理都默认依赖 `dataset_stats.pkl`。数据集更新后重算：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
|
||||||
|
--dataset_dir /home/droid/project/diana_sim/sim_transfer
|
||||||
|
```
|
||||||
|
|
||||||
|
远端只要把 `--dataset_dir` 换成对应主机路径即可。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 6. SwanLab 行为
|
||||||
|
|
||||||
|
当前配置默认值里 `train.use_swanlab=false`，但本分支常用 recipe 基本都显式开：
|
||||||
|
- `train.use_swanlab=true`
|
||||||
|
- `train.swanlab_project=roboimi-vla`
|
||||||
|
- `train.swanlab_run_name=<run_name>`
|
||||||
|
|
||||||
|
`train_vla.py` 的 SwanLab 行为：
|
||||||
|
- 初始化时上传 `train` / `data` / `agent` 三段 config
|
||||||
|
- 训练中记录：
|
||||||
|
- `train/loss`
|
||||||
|
- `train/lr`
|
||||||
|
- `train/best_loss`
|
||||||
|
- `train/step`
|
||||||
|
- checkpoint 验证时记录：
|
||||||
|
- `val/loss`
|
||||||
|
- held-out 数值验证时记录：
|
||||||
|
- `val/action_mse`
|
||||||
|
- rollout 验证时记录：
|
||||||
|
- `rollout/avg_reward`
|
||||||
|
- `rollout/epoch`
|
||||||
|
- 训练结束时记录：
|
||||||
|
- `final/checkpoint_path`
|
||||||
|
- `final/best_checkpoint_path`
|
||||||
|
|
||||||
|
训练期 rollout 生成的前视图轨迹 PNG 会 best-effort 上传到 SwanLab；失败只会 warning，不会让训练中断。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 7. 并行 rollout 说明
|
||||||
|
|
||||||
|
### 7.1 这套能力从哪里来
|
||||||
|
|
||||||
|
本分支的并行 rollout 方向不是 DataLoader 并行，而是 **`eval_vla.py` 的 multiprocess rollout path**。
|
||||||
|
参考来源：
|
||||||
|
`/home/droid/project/roboimi/.worktrees/multiprocess-rollout/roboimi/demos/vla_scripts/eval_vla.py`
|
||||||
|
|
||||||
|
那条路径的控制参数是：
|
||||||
|
- `eval.num_workers`
|
||||||
|
- `eval.cuda_devices`
|
||||||
|
|
||||||
|
语义是：
|
||||||
|
- `eval.num_workers`：环境 worker 数，按 episode 切分
|
||||||
|
- `eval.cuda_devices`：推理 server 绑定到哪些逻辑 GPU
|
||||||
|
|
||||||
|
### 7.2 两种常见模式
|
||||||
|
|
||||||
|
1. **单机单卡，多 worker 共用同一张 GPU**
|
||||||
|
- 典型：本地 5090 只有 1 卡，但想让 4 个 rollout worker 并行跑环境
|
||||||
|
- 形式：`eval.device=cuda eval.num_workers=4 'eval.cuda_devices=[0]'`
|
||||||
|
- 这时是 **1 个 CUDA inference server + 4 个 env worker**
|
||||||
|
|
||||||
|
2. **单机多卡，多 server 分摊 worker**
|
||||||
|
- 典型：5880 有 2 卡，L20 有多卡
|
||||||
|
- 形式：`eval.device=cuda eval.num_workers=8 'eval.cuda_devices=[0,1]'`
|
||||||
|
- worker 会按 round-robin 分到多个 server 上
|
||||||
|
|
||||||
|
### 7.3 操作上要注意什么
|
||||||
|
|
||||||
|
- 并行 rollout 依赖 **多进程 eval 路径**，不是 `train.num_workers`
|
||||||
|
- `train.num_workers` 是 DataLoader worker，和 rollout 并行不是一回事
|
||||||
|
- `eval.num_workers > 1` 时必须 `eval.headless=true`
|
||||||
|
- worker 数会自动 cap 到 `eval.num_episodes`
|
||||||
|
- multiprocess rollout 当前已经支持 **per-episode trajectory image PNG**；多 worker 时每个 worker 会在自己的 artifact 子目录下写图，summary 会带回对应路径
|
||||||
|
- 但多 worker 时仍然不要同时要求：
|
||||||
|
- `eval.record_video=true`
|
||||||
|
- `eval.save_trajectory=true`
|
||||||
|
- `eval.save_trajectory_npz=true`
|
||||||
|
- `eval.save_trajectory_image=true` 现在是可以开的，适合并行 reward + 定性检查一起做
|
||||||
|
|
||||||
|
### 7.4 并行 rollout 命令模板
|
||||||
|
|
||||||
|
**5090 单卡 4 worker：**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
||||||
|
train.device=cuda eval.device=cuda eval.headless=true eval.verbose_action=false \
|
||||||
|
eval.ckpt_path=/home/droid/project/roboimi/.worktrees/feat-lewm-imf-fusion/runs/<run_name>/checkpoints/vla_model_best.pt \
|
||||||
|
eval.num_episodes=10 eval.num_workers=4 'eval.cuda_devices=[0]' \
|
||||||
|
eval.save_summary_json=true eval.artifact_dir=/tmp/lewm_parallel_eval_5090
|
||||||
|
```
|
||||||
|
|
||||||
|
**5880 双卡 8 worker：**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
/home/droid/miniforge3/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/home/droid/sim_dataset/sim_transfer \
|
||||||
|
train.device=cuda eval.device=cuda eval.headless=true eval.verbose_action=false \
|
||||||
|
eval.ckpt_path=/home/droid/roboimi_suite_20260416_lewm_imf_fusion/runs/<run_name>/checkpoints/vla_model_best.pt \
|
||||||
|
eval.num_episodes=10 eval.num_workers=8 'eval.cuda_devices=[0,1]' \
|
||||||
|
eval.save_summary_json=true eval.artifact_dir=/tmp/lewm_parallel_eval_5880
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 8. 当前常用命令 / 脚本
|
||||||
|
|
||||||
|
### 8.1 本地 5090：直接用 suite 脚本
|
||||||
|
|
||||||
|
现成脚本：
|
||||||
|
`experiment_suites/2026-04-21-lewm-fromscratch-old9-epoch50-roll5-val-20260421-153037/launch_local_5090.sh`
|
||||||
|
|
||||||
|
运行：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
bash experiment_suites/2026-04-21-lewm-fromscratch-old9-epoch50-roll5-val-20260421-153037/launch_local_5090.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
### 8.2 本地 5090：手动启动同 recipe
|
||||||
|
|
||||||
|
```bash
|
||||||
|
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
||||||
|
'agent.lewm_query_offsets=[8]' \
|
||||||
|
agent.pred_horizon=8 \
|
||||||
|
agent.num_action_steps=8 \
|
||||||
|
train.device=cuda \
|
||||||
|
train.batch_size=32 \
|
||||||
|
train.lr=0.0001 \
|
||||||
|
train.max_steps=109350 \
|
||||||
|
train.num_workers=4 \
|
||||||
|
train.save_freq=10000 \
|
||||||
|
train.rollout_validate_on_checkpoint=false \
|
||||||
|
train.rollout_val_freq_epochs=5 \
|
||||||
|
train.rollout_num_episodes=10 \
|
||||||
|
train.val_split=0.0 \
|
||||||
|
'train.val_episode_indices=[100]' \
|
||||||
|
train.action_mse_val_freq_epochs=1 \
|
||||||
|
train.use_swanlab=true \
|
||||||
|
train.swanlab_project=roboimi-vla \
|
||||||
|
train.swanlab_run_name=lewmimf-q08-ph08-ex08-emb384-l12-fromscratch-epoch50-step109350-5090g0-20260421-153037 \
|
||||||
|
train.pretrained_ckpt=null \
|
||||||
|
agent.lewm_pretrained_ckpt=null \
|
||||||
|
hydra.run.dir=/home/droid/project/roboimi/.worktrees/feat-lewm-imf-fusion/runs/lewmimf-q08-ph08-ex08-emb384-l12-fromscratch-epoch50-step109350-5090g0-20260421-153037
|
||||||
|
```
|
||||||
|
|
||||||
|
### 8.3 5880：常用命令模板
|
||||||
|
|
||||||
|
```bash
|
||||||
|
ssh droid@100.73.14.65
|
||||||
|
cd /home/droid/roboimi_suite_20260416_lewm_imf_fusion
|
||||||
|
/home/droid/miniforge3/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/home/droid/sim_dataset/sim_transfer \
|
||||||
|
'agent.lewm_query_offsets=[8]' \
|
||||||
|
agent.pred_horizon=16 \
|
||||||
|
agent.num_action_steps=8 \
|
||||||
|
train.device=cuda train.batch_size=32 train.lr=0.0001 train.max_steps=109350 \
|
||||||
|
train.num_workers=4 train.save_freq=10000 train.rollout_validate_on_checkpoint=false \
|
||||||
|
train.rollout_val_freq_epochs=5 train.rollout_num_episodes=10 train.val_split=0.0 \
|
||||||
|
'train.val_episode_indices=[100]' train.action_mse_val_freq_epochs=1 \
|
||||||
|
train.use_swanlab=true train.swanlab_project=roboimi-vla \
|
||||||
|
train.swanlab_run_name=lewmimf-q08-ph16-ex08-emb384-l12-fromscratch-epoch50-step109350-5880g0-20260421-153037 \
|
||||||
|
train.pretrained_ckpt=null agent.lewm_pretrained_ckpt=null \
|
||||||
|
hydra.run.dir=/home/droid/roboimi_suite_20260416_lewm_imf_fusion/runs/lewmimf-q08-ph16-ex08-emb384-l12-fromscratch-epoch50-step109350-5880g0-20260421-153037
|
||||||
|
```
|
||||||
|
|
||||||
|
### 8.4 L20：常用命令模板
|
||||||
|
|
||||||
|
```bash
|
||||||
|
ssh droid@100.119.99.14
|
||||||
|
cd /data/roboimi_suite_20260416_lewm_imf_fusion
|
||||||
|
/home/droid/miniforge3/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/data/simtransfer/current \
|
||||||
|
'agent.lewm_query_offsets=[16]' \
|
||||||
|
agent.pred_horizon=16 \
|
||||||
|
agent.num_action_steps=16 \
|
||||||
|
train.device=cuda train.batch_size=32 train.lr=0.0001 train.max_steps=109350 \
|
||||||
|
train.num_workers=4 train.save_freq=10000 train.rollout_validate_on_checkpoint=false \
|
||||||
|
train.rollout_val_freq_epochs=5 train.rollout_num_episodes=10 train.val_split=0.0 \
|
||||||
|
'train.val_episode_indices=[100]' train.action_mse_val_freq_epochs=1 \
|
||||||
|
train.use_swanlab=true train.swanlab_project=roboimi-vla \
|
||||||
|
train.swanlab_run_name=lewmimf-q16-ph16-ex16-emb384-l12-fromscratch-epoch50-step109350-l20g0-20260421-153037 \
|
||||||
|
train.pretrained_ckpt=null agent.lewm_pretrained_ckpt=null \
|
||||||
|
hydra.run.dir=/data/roboimi_suite_20260416_lewm_imf_fusion/runs/lewmimf-q16-ph16-ex16-emb384-l12-fromscratch-epoch50-step109350-l20g0-20260421-153037
|
||||||
|
```
|
||||||
|
|
||||||
|
### 8.5 单次离线验证（当前分支已支持并行）
|
||||||
|
|
||||||
|
**单 GPU / 4 worker：**
|
||||||
|
|
||||||
|
```bash
|
||||||
|
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
||||||
|
train.device=cuda eval.device=cuda \
|
||||||
|
eval.ckpt_path=/home/droid/project/roboimi/.worktrees/feat-lewm-imf-fusion/runs/<run_name>/checkpoints/vla_model_best.pt \
|
||||||
|
eval.num_episodes=10 eval.num_workers=4 'eval.cuda_devices=[0]' \
|
||||||
|
eval.headless=true eval.verbose_action=false \
|
||||||
|
eval.save_summary_json=true eval.save_trajectory_image=true \
|
||||||
|
eval.trajectory_image_camera_name=front \
|
||||||
|
eval.artifact_dir=/tmp/lewm_eval_front
|
||||||
|
```
|
||||||
|
|
||||||
|
**训练内启用并行 GPU rollout（推荐显式写清楚）**：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
||||||
|
agent=lewm_resnet_query_imf_attnres \
|
||||||
|
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
||||||
|
'agent.lewm_query_offsets=[8]' \
|
||||||
|
agent.pred_horizon=8 \
|
||||||
|
agent.num_action_steps=8 \
|
||||||
|
train.device=cuda \
|
||||||
|
train.batch_size=32 \
|
||||||
|
train.lr=0.0001 \
|
||||||
|
train.max_steps=109350 \
|
||||||
|
train.num_workers=4 \
|
||||||
|
train.save_freq=10000 \
|
||||||
|
train.rollout_val_freq_epochs=5 \
|
||||||
|
train.rollout_num_episodes=10 \
|
||||||
|
train.rollout_device=cuda \
|
||||||
|
train.rollout_num_workers=4 \
|
||||||
|
'train.rollout_cuda_devices=[0]' \
|
||||||
|
train.rollout_validate_on_checkpoint=false \
|
||||||
|
train.val_split=0.0 \
|
||||||
|
'train.val_episode_indices=[100]' \
|
||||||
|
train.action_mse_val_freq_epochs=1 \
|
||||||
|
train.use_swanlab=true \
|
||||||
|
train.swanlab_project=roboimi-vla \
|
||||||
|
train.swanlab_run_name=<run_name> \
|
||||||
|
hydra.run.dir=/home/droid/project/roboimi/.worktrees/feat-lewm-imf-fusion/runs/<run_name>
|
||||||
|
```
|
||||||
|
|
||||||
|
### 8.6 监控日志
|
||||||
|
|
||||||
|
```bash
|
||||||
|
tail -f runs/<run_name>/launch.stdout.log
|
||||||
|
tail -f runs/<run_name>/train_vla.log
|
||||||
|
```
|
||||||
|
|
||||||
|
远端就把 `runs/<run_name>` 换成 manifest 里的绝对路径。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 9. 操作建议
|
||||||
|
|
||||||
|
- **优先以 suite 的 `manifest.json` / `notes.md` / `launch_logs/*.launch.log` 为准**，不要手写一套和历史 run 不一致的命令
|
||||||
|
- 要做当前常用验证，就显式加上：
|
||||||
|
- `train.val_split=0.0`
|
||||||
|
- `train.val_episode_indices=[100]`
|
||||||
|
- `train.action_mse_val_freq_epochs=1`
|
||||||
|
- `train.rollout_val_freq_epochs=5`
|
||||||
|
- `train.rollout_num_episodes=10`
|
||||||
|
- 本分支如果要对比不同 horizon / action-step，尽量只改：
|
||||||
|
- `agent.lewm_query_offsets`
|
||||||
|
- `agent.pred_horizon`
|
||||||
|
- `agent.num_action_steps`
|
||||||
|
- 想复现 2026-04-21 那轮 from-scratch 结果时，记得同时设：
|
||||||
|
- `train.pretrained_ckpt=null`
|
||||||
|
- `agent.lewm_pretrained_ckpt=null`
|
||||||
@@ -1,256 +0,0 @@
|
|||||||
# Align ResNet Transformer Diffusion To External Repo Implementation Plan
|
|
||||||
|
|
||||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development to implement this plan task-by-task. Do not switch to inline execution. Steps use checkbox (`- [ ]`) syntax for tracking.
|
|
||||||
|
|
||||||
**Goal:** 将当前仓库的 `resnet_transformer` 对齐到 external repo `/home/droid/project/diffusion_policy` 的原生 DDPM Transformer Diffusion（非 PMF、非 DiT、非 UNet）实现，并采用 external 中已存在的 full-attention / nocausal 变体（`causal_attn=false`），固定为三相机图像条件输入，同时保持 EE action 语义的推理执行路径正确。
|
|
||||||
|
|
||||||
**Architecture:** 保留当前仓库较轻量的训练脚本和数据集组织，但把 Transformer denoiser 本体对齐到 external repo 的 `TransformerForDiffusion` 实现与接口语义；视觉编码器保留当前仓库的 ResNet+SpatialSoftmax 路线，仅保证其输出维度与三相机条件输入兼容。评估路径统一按 EE action 调 `env.step(action)`，训练/推理配置显式固定三相机 `r_vis/top/front` 且图像永远作为条件输入。
|
|
||||||
|
|
||||||
**Tech Stack:** Python, PyTorch, diffusers DDPM/DDIM, Hydra/OmegaConf, unittest, h5py, OpenCV
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 0: 执行前提与分支约束
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Verify only
|
|
||||||
|
|
||||||
- [ ] **Step 1: Confirm execution stays on the feature branch**
|
|
||||||
|
|
||||||
Run: `git branch --show-current`
|
|
||||||
Expected: `feat-align-dp-transformer-ee`
|
|
||||||
|
|
||||||
If the current branch is not `feat-align-dp-transformer-ee`, create/switch to it before continuing:
|
|
||||||
|
|
||||||
Run: `git checkout -b feat-align-dp-transformer-ee || git checkout feat-align-dp-transformer-ee`
|
|
||||||
Expected: working branch becomes `feat-align-dp-transformer-ee`
|
|
||||||
|
|
||||||
- [ ] **Step 2: Confirm implementation is executed with subagents**
|
|
||||||
|
|
||||||
Use `superpowers:subagent-driven-development` for implementation tasks and reviews. Do not switch to inline/manual execution unless the human explicitly changes course.
|
|
||||||
|
|
||||||
- [ ] **Step 3: Confirm the current branch is the only target branch for this work**
|
|
||||||
|
|
||||||
Do not create or switch to another branch/worktree during implementation unless the human explicitly asks for it. All changes for this migration stay on `feat-align-dp-transformer-ee`.
|
|
||||||
|
|
||||||
### Task 1: 验证 EE action 推理执行语义已固定
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Verify: `roboimi/vla/eval_utils.py`
|
|
||||||
- Verify: `roboimi/demos/vla_scripts/eval_vla.py`
|
|
||||||
- Verify: `tests/test_eval_vla_execution.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: Verify the test still passes on the feature branch**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_eval_vla_execution -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
- [ ] **Step 2: Verify the real eval script no longer executes joint-action stepping**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
python - <<'PY'
|
|
||||||
from pathlib import Path
|
|
||||||
src = Path('roboimi/demos/vla_scripts/eval_vla.py').read_text()
|
|
||||||
assert 'execute_policy_action(env, action)' in src
|
|
||||||
assert 'env.step_jnt(action)' not in src
|
|
||||||
print('eval_vla execution path verified')
|
|
||||||
PY
|
|
||||||
```
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
### Task 2: 通过对拍测试把当前 Transformer head 对齐到 external repo 的 `TransformerForDiffusion`
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/vla/models/heads/transformer1d.py`
|
|
||||||
- Modify: `roboimi/vla/conf/head/transformer1d.yaml`
|
|
||||||
- Test: `tests/test_transformer1d_external_alignment.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: Confirm the alignment test exists and captures the intended parity contract**
|
|
||||||
|
|
||||||
Write a test that imports external repo's `TransformerForDiffusion` from:
|
|
||||||
`/home/droid/project/diffusion_policy/diffusion_policy/model/diffusion/transformer_for_diffusion.py`
|
|
||||||
and verifies the local `Transformer1D` can load the external model's `state_dict` and produce numerically identical outputs for the same inputs when configured equivalently. Use the full-attention / nocausal configuration (`causal_attn=False`) as the target behavior.
|
|
||||||
|
|
||||||
Required assertions:
|
|
||||||
- same parameter key structure (or compatible `load_state_dict(strict=True)`)
|
|
||||||
- same output shape `(B, T, action_dim)`
|
|
||||||
- `torch.allclose(local_out, external_out, atol=1e-6, rtol=1e-5)`
|
|
||||||
- local model exposes `get_optim_groups(weight_decay=...)` like external repo
|
|
||||||
- use an explicit import path / loader that does not depend on installing the external repo as a package
|
|
||||||
- account for external `ModuleAttrMixin`-style optimizer grouping expectations, including `_dummy_variable` / no-decay bookkeeping if needed for strict state-dict parity
|
|
||||||
- set both models to `eval()` and fix the random seed inside the test so dropout does not create false mismatches
|
|
||||||
|
|
||||||
- [ ] **Step 2: If the test still fails on this branch, use that red state as the TDD starting point**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_transformer1d_external_alignment -v`
|
|
||||||
Expected: either FAIL before implementation on a fresh checkout, or PASS if Task 2 has already been completed on this branch.
|
|
||||||
|
|
||||||
- [ ] **Step 3a: Port API and state-dict compatibility first**
|
|
||||||
|
|
||||||
Match constructor args, parameter names, embeddings, masks, and strict `state_dict` layout with external `TransformerForDiffusion`.
|
|
||||||
|
|
||||||
- [ ] **Step 3b: Port `_init_weights` and optimizer grouping**
|
|
||||||
|
|
||||||
Match external `_init_weights`, `get_optim_groups`, and `configure_optimizers`.
|
|
||||||
|
|
||||||
- [ ] **Step 3c: Port forward semantics**
|
|
||||||
|
|
||||||
Match external `forward(sample, timestep, cond)` behavior under the full-attention / nocausal configuration.
|
|
||||||
|
|
||||||
- [ ] **Step 3d: Port the minimal external implementation**
|
|
||||||
|
|
||||||
Update `roboimi/vla/models/heads/transformer1d.py` so it matches external repo's native DDPM transformer implementation semantics:
|
|
||||||
- same constructor arguments and defaults where relevant
|
|
||||||
- same token accounting (`time_as_cond`, `obs_as_cond`, `T_cond`)
|
|
||||||
- same parameter naming/layout for embeddings, encoder, decoder, masks
|
|
||||||
- same `_init_weights`
|
|
||||||
- same `get_optim_groups` / `configure_optimizers`
|
|
||||||
- same `forward(sample, timestep, cond)` behavior
|
|
||||||
|
|
||||||
Do **not** port PMF / IMF / DiT branches.
|
|
||||||
|
|
||||||
- [ ] **Step 4: Run test to verify it passes**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_transformer1d_external_alignment -v`
|
|
||||||
Expected: PASS with strict state-dict load and matching outputs.
|
|
||||||
|
|
||||||
### Task 3: 将当前 Agent 与配置固定到“三相机图像作为条件”的 Transformer diffusion 路线
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/vla/agent.py`
|
|
||||||
- Modify: `roboimi/vla/conf/agent/resnet_transformer.yaml`
|
|
||||||
- Modify: `roboimi/vla/conf/data/simpe_robot_dataset.yaml`
|
|
||||||
- Modify: `roboimi/vla/conf/eval/eval.yaml`
|
|
||||||
- Modify: `roboimi/vla/models/backbones/resnet_diffusion.py`
|
|
||||||
- Test: `tests/test_resnet_transformer_agent_wiring.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write the failing test**
|
|
||||||
|
|
||||||
Write a wiring test that instantiates the Transformer agent config and checks:
|
|
||||||
- `head_type == "transformer"`
|
|
||||||
- `cfg.data.camera_names == cfg.eval.camera_names == ["r_vis", "top", "front"]`
|
|
||||||
- `num_cams == 3`
|
|
||||||
- Transformer `cond_dim == single_cam_feat_dim * 3 + obs_dim`
|
|
||||||
- `predict_action(...)` accepts image conditions and returns `(B, pred_horizon, action_dim)`
|
|
||||||
- test setup must not download weights; override `pretrained_backbone_weights=null` or stub the backbone
|
|
||||||
- assert camera order used for conditioning is tied to the required three cameras, not a stray constant
|
|
||||||
|
|
||||||
- [ ] **Step 2: Run test to verify it fails**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_resnet_transformer_agent_wiring -v`
|
|
||||||
Expected: FAIL if configs/head wiring do not yet guarantee three-camera conditional setup.
|
|
||||||
|
|
||||||
- [ ] **Step 3: Implement minimal alignment**
|
|
||||||
|
|
||||||
Make the transformer path explicit and stable:
|
|
||||||
- keep image observations always as condition (`cond_dim > 0`, `obs_as_cond` semantics)
|
|
||||||
- keep exactly three cameras: `r_vis`, `top`, `front`
|
|
||||||
- keep current ResNet+SpatialSoftmax backbone unless a test proves incompatibility
|
|
||||||
- make the camera feature order deterministic and aligned to the required three-camera list, not generic key sorting
|
|
||||||
- ensure `agent.per_step_cond_dim` and config `head.cond_dim` stay consistent
|
|
||||||
- ensure eval config uses the same three camera names as training
|
|
||||||
- ensure transformer config follows the external full-attention variant (`causal_attn=false`) instead of the external default causal setting
|
|
||||||
|
|
||||||
- [ ] **Step 4: Run test to verify it passes**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_resnet_transformer_agent_wiring -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
### Task 4: 让训练脚本在 Transformer 路线上尽量遵循 external repo 的 optimizer/head 使用方式
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/demos/vla_scripts/train_vla.py`
|
|
||||||
- Test: `tests/test_train_vla_transformer_optimizer.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write the failing test**
|
|
||||||
|
|
||||||
Write a test that builds a transformer agent and verifies the training script prefers the head/model supplied optimizer grouping when available (via `get_optim_groups`) instead of blindly using one flat `AdamW(agent.parameters(), ...)`.
|
|
||||||
The test must also prove that every remaining trainable non-head parameter is included exactly once in the optimizer (no silent drops, no duplicates).
|
|
||||||
|
|
||||||
- [ ] **Step 2: Run test to verify it fails**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_train_vla_transformer_optimizer -v`
|
|
||||||
Expected: FAIL because current training script uses flat optimizer construction.
|
|
||||||
|
|
||||||
- [ ] **Step 3: Implement minimal optimizer alignment**
|
|
||||||
|
|
||||||
Update `train_vla.py` so that:
|
|
||||||
- for transformer head paths, if `agent.noise_pred_net` exposes `get_optim_groups`, build optimizer groups like external repo
|
|
||||||
- explicitly include the remaining trainable non-head parameters (for example the ResNet backbone's non-frozen projection / pooling layers) in the optimizer instead of accidentally dropping them
|
|
||||||
- keep the rest of the simple training loop intact (no EMA unless required later)
|
|
||||||
- do not over-port external workspace abstractions
|
|
||||||
|
|
||||||
- [ ] **Step 4: Run test to verify it passes**
|
|
||||||
|
|
||||||
Run: `mamba run -n roboimi python -m unittest tests.test_train_vla_transformer_optimizer -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
### Task 5: 端到端实例化与最小推理验收
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Verify only
|
|
||||||
|
|
||||||
- [ ] **Step 1: Run all focused unit tests**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
mamba run -n roboimi python -m unittest \
|
|
||||||
tests.test_eval_vla_execution \
|
|
||||||
tests.test_transformer1d_external_alignment \
|
|
||||||
tests.test_resnet_transformer_agent_wiring \
|
|
||||||
tests.test_train_vla_transformer_optimizer -v
|
|
||||||
```
|
|
||||||
Expected: all PASS
|
|
||||||
|
|
||||||
- [ ] **Step 2: Run syntax checks on changed training/eval/model files**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
mamba run -n roboimi python -m py_compile \
|
|
||||||
roboimi/vla/eval_utils.py \
|
|
||||||
roboimi/vla/models/heads/transformer1d.py \
|
|
||||||
roboimi/vla/agent.py \
|
|
||||||
roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
roboimi/demos/vla_scripts/eval_vla.py
|
|
||||||
```
|
|
||||||
Expected: no syntax errors
|
|
||||||
|
|
||||||
- [ ] **Step 3: Run a local instantiation smoke test**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
|
|
||||||
import torch
|
|
||||||
from hydra import compose, initialize_config_dir
|
|
||||||
from hydra.utils import instantiate
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
config_dir = str((Path.cwd() / "roboimi" / "vla" / "conf").resolve())
|
|
||||||
with initialize_config_dir(version_base=None, config_dir=config_dir):
|
|
||||||
cfg = compose(config_name="config", overrides=[
|
|
||||||
"agent=resnet_transformer",
|
|
||||||
"agent.vision_backbone.pretrained_backbone_weights=null",
|
|
||||||
])
|
|
||||||
|
|
||||||
agent = instantiate(cfg.agent, dataset_stats=None)
|
|
||||||
images = {
|
|
||||||
"r_vis": torch.rand(1, cfg.agent.obs_horizon, 3, 224, 224),
|
|
||||||
"top": torch.rand(1, cfg.agent.obs_horizon, 3, 224, 224),
|
|
||||||
"front": torch.rand(1, cfg.agent.obs_horizon, 3, 224, 224),
|
|
||||||
}
|
|
||||||
qpos = torch.rand(1, cfg.agent.obs_horizon, cfg.agent.obs_dim)
|
|
||||||
out = agent.predict_action(images, qpos)
|
|
||||||
assert out.shape == (1, cfg.agent.pred_horizon, cfg.agent.action_dim), out.shape
|
|
||||||
print("smoke_shape=", out.shape)
|
|
||||||
PY
|
|
||||||
```
|
|
||||||
|
|
||||||
that:
|
|
||||||
- instantiates `agent=resnet_transformer`
|
|
||||||
- constructs fake three-camera input (`r_vis`, `top`, `front`)
|
|
||||||
- calls `agent.predict_action(...)`
|
|
||||||
- verifies output shape is `(1, pred_horizon, 16)`
|
|
||||||
|
|
||||||
Expected: PASS
|
|
||||||
@@ -1,202 +0,0 @@
|
|||||||
# VLA Experiment Sweep Execution Plan
|
|
||||||
|
|
||||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
|
||||||
|
|
||||||
**Goal:** Probe the largest safe shared batch size, prepare the 10 unique VLA sweep runs, and launch the serial GPU experiment queue with SwanLab logging and 20-epoch headless rollout validation.
|
|
||||||
|
|
||||||
**Architecture:** Use the existing `train_vla.py` / `eval_vla.py` path without changing model code unless launch-blocking issues appear. A small external launcher contract will manage per-run directories, logs, pids, and serial execution while Hydra places trainer-local `checkpoints/` under each run directory.
|
|
||||||
|
|
||||||
**Tech Stack:** zsh, mamba env `roboimi`, Hydra overrides, PyTorch CUDA, SwanLab, existing `train_vla.py`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 1: Reconfirm schedule math and current runtime assumptions
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `docs/superpowers/specs/2026-03-30-vla-experiment-sweep-design.md` (only if execution reveals a spec mismatch)
|
|
||||||
- Test: none
|
|
||||||
|
|
||||||
- [ ] **Step 1: Print dataset size and epoch math for the baseline batch size**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
python - <<'PY'
|
|
||||||
num_samples = 70000
|
|
||||||
for batch_size in (32, 48, 64, 80):
|
|
||||||
steps_per_epoch = num_samples // batch_size
|
|
||||||
print(batch_size, steps_per_epoch, 20 * steps_per_epoch, 200 * steps_per_epoch)
|
|
||||||
PY
|
|
||||||
```
|
|
||||||
|
|
||||||
Expected: integer epoch-step mappings for candidate batch sizes.
|
|
||||||
|
|
||||||
- [ ] **Step 2: Reconfirm no stale training process is already active**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
ps -ef | grep -E 'train_vla.py|python .*roboimi/demos/vla_scripts/train_vla.py' | grep -v grep || true
|
|
||||||
```
|
|
||||||
|
|
||||||
Expected: no active training process unless intentionally started by this session.
|
|
||||||
|
|
||||||
- [ ] **Step 3: Record the execution assumptions**
|
|
||||||
|
|
||||||
Capture in session notes:
|
|
||||||
- dataset path
|
|
||||||
- camera list
|
|
||||||
- current branch
|
|
||||||
- no active conflicting training process
|
|
||||||
|
|
||||||
|
|
||||||
### Task 2: Probe the largest safe shared batch size
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Create: `runs/batch-probe-<timestamp>/` (runtime artifact)
|
|
||||||
- Test: probe command output only
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write the candidate probe command for the largest model**
|
|
||||||
|
|
||||||
Use overrides:
|
|
||||||
- `data.dataset_dir=/home/droid/project/diana_sim/sim_transfer`
|
|
||||||
- `data.camera_names=[r_vis,top,front]`
|
|
||||||
- `agent.head.n_emb=384`
|
|
||||||
- `agent.head.n_layer=12`
|
|
||||||
- `agent.head.n_head=4`
|
|
||||||
- `agent.vision_backbone.pretrained_backbone_weights=IMAGENET1K_V1`
|
|
||||||
- `agent.vision_backbone.freeze_backbone=true`
|
|
||||||
- `agent.vision_backbone.use_separate_rgb_encoder_per_camera=true`
|
|
||||||
- `train.device=cuda`
|
|
||||||
- `train.val_split=0.0`
|
|
||||||
- `train.seed=42`
|
|
||||||
- `train.use_swanlab=false`
|
|
||||||
- `train.rollout_val_freq_epochs=0`
|
|
||||||
- `train.rollout_validate_on_checkpoint=false`
|
|
||||||
- `train.max_steps=4`
|
|
||||||
|
|
||||||
- [ ] **Step 2: Run the probe for `batch_size=32`**
|
|
||||||
|
|
||||||
Expected pass condition:
|
|
||||||
- no CUDA OOM
|
|
||||||
- no crash/abort
|
|
||||||
- no NaN/Inf loss
|
|
||||||
- 4 steps complete
|
|
||||||
|
|
||||||
- [ ] **Step 3: Repeat for `batch_size=48`, `64`, then optionally `80`**
|
|
||||||
|
|
||||||
Stop increasing once one candidate fails.
|
|
||||||
|
|
||||||
- [ ] **Step 4: Compute the shared LR and 200-epoch step budget**
|
|
||||||
|
|
||||||
Using:
|
|
||||||
```text
|
|
||||||
lr = 1e-4 * (batch_size / 16)
|
|
||||||
max_steps = 200 * floor(70000 / batch_size)
|
|
||||||
```
|
|
||||||
|
|
||||||
- [ ] **Step 5: Write down the chosen shared batch size and derived max_steps**
|
|
||||||
|
|
||||||
This becomes the execution contract for all runs.
|
|
||||||
|
|
||||||
|
|
||||||
### Task 3: Materialize the 10 unique run matrix
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Create: `runs/vla-sweep-<timestamp>/manifest.txt` (runtime artifact)
|
|
||||||
- Create: `runs/vla-sweep-<timestamp>/launch_queue.sh` (runtime artifact)
|
|
||||||
- Test: shell syntax / manifest inspection
|
|
||||||
|
|
||||||
- [ ] **Step 1: Enumerate the 2-point pretraining comparison**
|
|
||||||
|
|
||||||
Canonical runs:
|
|
||||||
1. `sim-transfer-baseline-pretrain-on-emb128-layer4`
|
|
||||||
2. `sim-transfer-pretrain-off`
|
|
||||||
|
|
||||||
- [ ] **Step 2: Enumerate the remaining 8 unique architecture-sweep runs**
|
|
||||||
|
|
||||||
Skip the duplicate `(pretrain-on, emb128, layer4)` because it reuses the baseline run.
|
|
||||||
|
|
||||||
- [ ] **Step 3: For each run, define exact Hydra overrides**
|
|
||||||
|
|
||||||
Every run must pin:
|
|
||||||
- dataset path
|
|
||||||
- three cameras
|
|
||||||
- `train.device=cuda`
|
|
||||||
- `train.val_split=0.0`
|
|
||||||
- `train.num_workers=12`
|
|
||||||
- `train.seed=42`
|
|
||||||
- `train.rollout_val_freq_epochs=20`
|
|
||||||
- `train.rollout_validate_on_checkpoint=false`
|
|
||||||
- `train.rollout_num_episodes=3`
|
|
||||||
- `train.use_swanlab=true`
|
|
||||||
- a unique `train.swanlab_run_name`
|
|
||||||
- shared batch size
|
|
||||||
- shared LR
|
|
||||||
- derived `max_steps`
|
|
||||||
- `agent.head.n_head=4`
|
|
||||||
- `agent.vision_backbone.freeze_backbone=true`
|
|
||||||
- `agent.vision_backbone.use_separate_rgb_encoder_per_camera=true`
|
|
||||||
|
|
||||||
- [ ] **Step 4: Write the queue launcher script**
|
|
||||||
|
|
||||||
Launcher responsibilities:
|
|
||||||
- create run directory
|
|
||||||
- export `LD_LIBRARY_PATH` CUDA/cuDNN fixup
|
|
||||||
- export `MPLCONFIGDIR=/tmp/mpl`
|
|
||||||
- set `hydra.job.chdir=true`
|
|
||||||
- set `hydra.run.dir=<run_dir>`
|
|
||||||
- tee logs to `<run_dir>/train.log`
|
|
||||||
- write `<run_dir>/train.pid`
|
|
||||||
- run jobs serially
|
|
||||||
|
|
||||||
- [ ] **Step 5: Verify the queue script syntax**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
zsh -n runs/vla-sweep-<timestamp>/launch_queue.sh
|
|
||||||
```
|
|
||||||
|
|
||||||
Expected: exit 0.
|
|
||||||
|
|
||||||
|
|
||||||
### Task 4: Launch the first probe-selected production run and verify startup
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Create: `runs/vla-sweep-<timestamp>/<run-name>/` (runtime artifact)
|
|
||||||
- Test: live log inspection / process inspection
|
|
||||||
|
|
||||||
- [ ] **Step 1: Start the first run in the queue**
|
|
||||||
|
|
||||||
Use the generated launcher contract.
|
|
||||||
|
|
||||||
- [ ] **Step 2: Inspect the first 100-200 log lines**
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- config resolves correctly
|
|
||||||
- dataset loads
|
|
||||||
- agent initializes on CUDA
|
|
||||||
- training loop starts
|
|
||||||
- SwanLab initializes
|
|
||||||
|
|
||||||
- [ ] **Step 3: Confirm process identity and run directory**
|
|
||||||
|
|
||||||
Collect:
|
|
||||||
- PID
|
|
||||||
- run dir
|
|
||||||
- log path
|
|
||||||
- SwanLab run name
|
|
||||||
|
|
||||||
- [ ] **Step 4: If startup is clean, continue the remaining serial queue**
|
|
||||||
|
|
||||||
If startup fails, stop and debug before launching more runs.
|
|
||||||
|
|
||||||
|
|
||||||
### Task 5: Report the launched sweep contract back to the user
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Test: log evidence only
|
|
||||||
|
|
||||||
- [ ] **Step 1: Summarize the chosen batch size, LR, max_steps, and rollout cadence**
|
|
||||||
|
|
||||||
- [ ] **Step 2: Provide the active run directory / pid / first-run status**
|
|
||||||
|
|
||||||
- [ ] **Step 3: State the remaining queued runs by name**
|
|
||||||
@@ -1,454 +0,0 @@
|
|||||||
# VLA Training + Headless Rollout + SwanLab Implementation Plan
|
|
||||||
|
|
||||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
|
||||||
|
|
||||||
**Goal:** 补齐当前 `Transformer1D` 训练依赖，在 `/home/droid/project/diana_sim/sim_transfer` 上启动训练，接入 SwanLab 标量日志，并提供训练期可选的 headless rollout validation 路径。
|
|
||||||
|
|
||||||
**Architecture:** 保持现有 `train_vla.py` / `eval_vla.py` 主体不变，只做最小必要改造：补依赖、补 stats 生成入口、在训练脚本里加轻量 SwanLab logger 和可选 checkpoint-time rollout wrapper、在环境侧把图像更新与 GUI 显示解耦。训练默认仍走当前 `resnet_transformer + Transformer1D` 路线，rollout validation 作为薄封装默认关闭。
|
|
||||||
|
|
||||||
**Tech Stack:** Python, mamba/conda, pip, PyTorch, Hydra, diffusers, torchvision, einops, SwanLab, MuJoCo, OpenCV, unittest
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 0: 执行前提与分支/环境确认
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Verify only
|
|
||||||
|
|
||||||
- [ ] **Step 1: 确认当前分支仍是目标分支**
|
|
||||||
|
|
||||||
Run: `git branch --show-current`
|
|
||||||
Expected: `feat-align-dp-transformer-ee`
|
|
||||||
|
|
||||||
- [ ] **Step 2: 记录当前 Python 解释器与环境名**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
|
|
||||||
import sys
|
|
||||||
print(sys.executable)
|
|
||||||
PY`
|
|
||||||
Expected: 输出 `/home/droid/.conda/envs/roboimi/bin/python`
|
|
||||||
|
|
||||||
- [ ] **Step 3: 记录当前数据集目录存在性与 episode 数量**
|
|
||||||
|
|
||||||
Run: `/usr/bin/zsh -lc 'echo DATASET=/home/droid/project/diana_sim/sim_transfer; find /home/droid/project/diana_sim/sim_transfer -maxdepth 1 -name "episode_*.hdf5" | wc -l'`
|
|
||||||
Expected: 输出目录路径与 `100`
|
|
||||||
|
|
||||||
### Task 1: 补齐训练依赖并把 resolved versions 写回环境定义
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `environment.yml`
|
|
||||||
- Verify only: local `roboimi` env
|
|
||||||
|
|
||||||
- [ ] **Step 1: 写出缺失依赖的最小清单**
|
|
||||||
|
|
||||||
需要补齐：
|
|
||||||
- `diffusers`
|
|
||||||
- `torchvision`
|
|
||||||
- `einops`
|
|
||||||
- `swanlab`
|
|
||||||
|
|
||||||
- [ ] **Step 2: 先用 dry-run 解析候选版本，确认不会升级 Torch**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python -m pip install --dry-run \
|
|
||||||
diffusers torchvision einops swanlab
|
|
||||||
```
|
|
||||||
Expected: 输出候选版本；若显示会升级/替换 `torch`，则停止并改用显式兼容版本
|
|
||||||
|
|
||||||
- [ ] **Step 3: 安装与当前 Torch 兼容的缺失依赖到现有环境**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python -m pip install \
|
|
||||||
diffusers torchvision einops swanlab
|
|
||||||
```
|
|
||||||
Expected: 安装成功，且不替换当前 `torch==2.4.0`
|
|
||||||
|
|
||||||
- [ ] **Step 4: 运行 import 验证**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
|
|
||||||
mods=['torch','hydra','omegaconf','diffusers','torchvision','einops','cv2','h5py','swanlab','mujoco']
|
|
||||||
for m in mods:
|
|
||||||
__import__(m)
|
|
||||||
print('OK', m)
|
|
||||||
PY
|
|
||||||
```
|
|
||||||
Expected: 每个模块都输出 `OK <module>`
|
|
||||||
|
|
||||||
- [ ] **Step 5: 记录实际安装版本**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
|
|
||||||
import diffusers, torchvision, einops, swanlab
|
|
||||||
print('diffusers', getattr(diffusers,'__version__',''))
|
|
||||||
print('torchvision', getattr(torchvision,'__version__',''))
|
|
||||||
print('einops', getattr(einops,'__version__',''))
|
|
||||||
print('swanlab', getattr(swanlab,'__version__',''))
|
|
||||||
PY
|
|
||||||
```
|
|
||||||
Expected: 输出四个包的 resolved versions
|
|
||||||
|
|
||||||
- [ ] **Step 6: 将 resolved versions 写回 `environment.yml`**
|
|
||||||
|
|
||||||
把新增依赖补到 `environment.yml` 的现有依赖列表（若使用 `pip:` 段则更新该段）里，使用 Step 5 得到的**实际 resolved versions**，避免环境漂移，并避免重复 package 条目。
|
|
||||||
|
|
||||||
- [ ] **Step 7: 语法检查环境定义文件仅作结构确认**
|
|
||||||
|
|
||||||
Run: `python - <<'PY'
|
|
||||||
from pathlib import Path
|
|
||||||
text = Path('environment.yml').read_text()
|
|
||||||
assert 'diffusers' in text
|
|
||||||
assert 'torchvision' in text
|
|
||||||
assert 'einops' in text
|
|
||||||
assert 'swanlab' in text
|
|
||||||
print('environment.yml updated')
|
|
||||||
PY`
|
|
||||||
Expected: `environment.yml updated`
|
|
||||||
|
|
||||||
### Task 2: 让统计脚本支持外部数据目录并生成 dataset stats
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/vla/scripts/calculate_stats.py`
|
|
||||||
- Test: `tests/test_calculate_stats_cli.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: 写 failing test，验证统计脚本可接受外部 `--dataset_dir` 并输出目标路径**
|
|
||||||
|
|
||||||
Test file should:
|
|
||||||
- 用临时目录创建最小 HDF5 episode
|
|
||||||
- 调用脚本入口/函数时传入外部目录
|
|
||||||
- 断言输出 `dataset_stats.pkl` 出现在该目录
|
|
||||||
- 断言 pickle 内包含 `action_mean/qpos_mean/...`
|
|
||||||
|
|
||||||
- [ ] **Step 2: 跑测试确认它先失败**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_calculate_stats_cli -v`
|
|
||||||
Expected: FAIL（当前脚本写死默认目录）
|
|
||||||
|
|
||||||
- [ ] **Step 3: 最小实现 `--dataset_dir` 支持**
|
|
||||||
|
|
||||||
要求：
|
|
||||||
- 保留现有统计逻辑
|
|
||||||
- 仅增加 CLI 参数解析
|
|
||||||
- 输出仍写入 `<dataset_dir>/dataset_stats.pkl`
|
|
||||||
|
|
||||||
- [ ] **Step 4: 重新跑测试确认转绿**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_calculate_stats_cli -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
- [ ] **Step 5: 用真实数据集生成 stats**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
|
|
||||||
--dataset_dir /home/droid/project/diana_sim/sim_transfer
|
|
||||||
```
|
|
||||||
Expected: 生成 `/home/droid/project/diana_sim/sim_transfer/dataset_stats.pkl`
|
|
||||||
|
|
||||||
- [ ] **Step 6: 验证 stats 文件结构**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
|
|
||||||
import pickle
|
|
||||||
path='/home/droid/project/diana_sim/sim_transfer/dataset_stats.pkl'
|
|
||||||
with open(path,'rb') as f:
|
|
||||||
stats=pickle.load(f)
|
|
||||||
for k in ['action_mean','action_std','action_min','action_max','qpos_mean','qpos_std','qpos_min','qpos_max']:
|
|
||||||
assert k in stats, k
|
|
||||||
print('stats_ok')
|
|
||||||
PY
|
|
||||||
```
|
|
||||||
Expected: `stats_ok`
|
|
||||||
|
|
||||||
### Task 3: 增加 SwanLab 训练日志集成
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/demos/vla_scripts/train_vla.py`
|
|
||||||
- Modify: `roboimi/vla/conf/config.yaml`
|
|
||||||
- Test: `tests/test_train_vla_swanlab_logging.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: 写 failing test，验证训练脚本在 `train.use_swanlab=true` 时会初始化 SwanLab 并记录标量**
|
|
||||||
|
|
||||||
Test should:
|
|
||||||
- stub `swanlab`
|
|
||||||
- 调用训练脚本中抽取出的非 Hydra helper（如 `_run_training(cfg)`）的最小路径（`max_steps=0` 或很小）
|
|
||||||
- 断言调用了：
|
|
||||||
- `swanlab.init(project='roboimi-vla', ...)`
|
|
||||||
- 至少一次 `swanlab.log({...})`
|
|
||||||
- 断言当 `use_swanlab=false` 时不会 import/初始化 SwanLab
|
|
||||||
- 断言当 `use_swanlab=true` 且 import `swanlab` 失败时会 fail fast
|
|
||||||
- 断言当 `use_swanlab=true` 且认证/登录状态不可用时会 fail fast
|
|
||||||
- 断言会记录最终/最佳 checkpoint 路径到 log/summary
|
|
||||||
|
|
||||||
- [ ] **Step 2: 跑测试确认它先失败**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_swanlab_logging -v`
|
|
||||||
Expected: FAIL（当前无 SwanLab 集成）
|
|
||||||
|
|
||||||
- [ ] **Step 3: 在配置中增加最小 SwanLab 契约**
|
|
||||||
|
|
||||||
在 `roboimi/vla/conf/config.yaml` 添加：
|
|
||||||
- `train.use_swanlab: true`
|
|
||||||
- `train.swanlab_project: roboimi-vla`
|
|
||||||
- 可选 `train.swanlab_run_name: null`
|
|
||||||
|
|
||||||
- [ ] **Step 4: 从 Hydra 入口提取可测试的训练 helper**
|
|
||||||
|
|
||||||
要求：
|
|
||||||
- 新增类似 `_run_training(cfg)` 的普通函数
|
|
||||||
- `main()` 只做 Hydra 入口转发
|
|
||||||
- 测试只调用 helper，不直接调用 Hydra-decorated `main(cfg)`
|
|
||||||
|
|
||||||
- [ ] **Step 5: 在训练脚本中实现 SwanLab 初始化的 fail-fast 逻辑**
|
|
||||||
|
|
||||||
要求：
|
|
||||||
- `use_swanlab=true` 时：
|
|
||||||
- import 失败 -> 直接报错
|
|
||||||
- 本地未登录/认证失败 -> 直接报错
|
|
||||||
- 成功后执行 `swanlab.init(project=cfg.train.swanlab_project, ...)`
|
|
||||||
|
|
||||||
- [ ] **Step 6: 在训练脚本中实现轻量 scalar logger**
|
|
||||||
|
|
||||||
要求：
|
|
||||||
- 仅 scalar logging，不引入自定义 callback 框架
|
|
||||||
- 训练时记录 `train/loss`, `train/lr`, `train/best_loss`, `train/step`
|
|
||||||
- 验证时记录 `val/loss`
|
|
||||||
- 训练结束时记录：
|
|
||||||
- `train/final_checkpoint_path`
|
|
||||||
- `train/best_checkpoint_path`
|
|
||||||
- 若库支持则显式 `finish/close`
|
|
||||||
|
|
||||||
- [ ] **Step 7: 重新跑测试确认转绿**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_swanlab_logging -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
- [ ] **Step 8: 用提供的 API key 完成本地登录**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/usr/bin/zsh -lc 'SWANLAB_API_KEY="<user-provided>"; /home/droid/.conda/envs/roboimi/bin/swanlab login -k "$SWANLAB_API_KEY"'
|
|
||||||
```
|
|
||||||
Expected: 登录成功并保存本地凭证
|
|
||||||
|
|
||||||
### Task 4: 为评估/rollout 路径增加 headless 模式
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/envs/double_base.py`
|
|
||||||
- Modify: `roboimi/envs/double_pos_ctrl_env.py`
|
|
||||||
- Modify: `roboimi/vla/conf/eval/eval.yaml`
|
|
||||||
- Modify: `roboimi/demos/vla_scripts/eval_vla.py`
|
|
||||||
- Test: `tests/test_eval_vla_headless.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: 写 failing test，验证 headless 路径不触发 GUI 调用**
|
|
||||||
|
|
||||||
Test should stub:
|
|
||||||
- `cv2.namedWindow`
|
|
||||||
- `cv2.imshow`
|
|
||||||
- `cv2.waitKey`
|
|
||||||
- viewer launch/render path
|
|
||||||
|
|
||||||
And assert:
|
|
||||||
- `eval.headless=true` 时不调用这些 GUI 接口
|
|
||||||
- 仍能获取图像观测并走到 policy action 执行前/后关键路径
|
|
||||||
|
|
||||||
- [ ] **Step 2: 跑测试确认它先失败**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_eval_vla_headless -v`
|
|
||||||
Expected: FAIL（当前 env/eval 默认会开 viewer 和 cv2 窗口）
|
|
||||||
|
|
||||||
- [ ] **Step 3: 统一配置开关为 `eval.headless`**
|
|
||||||
|
|
||||||
在 `roboimi/vla/conf/eval/eval.yaml` 添加：
|
|
||||||
- `headless: false`
|
|
||||||
|
|
||||||
不要再引入第二个同义开关。
|
|
||||||
|
|
||||||
- [ ] **Step 4: 在 env 工厂中接入 `headless`**
|
|
||||||
|
|
||||||
在 `make_sim_env(...)` 中：
|
|
||||||
- `eval.headless=true` -> `is_render=False`
|
|
||||||
- 保留图像观测更新
|
|
||||||
- 不创建 MuJoCo viewer
|
|
||||||
|
|
||||||
- [ ] **Step 5: 将相机更新与 GUI 显示解耦**
|
|
||||||
|
|
||||||
在 `double_base.py`：
|
|
||||||
- 图像更新逻辑保留
|
|
||||||
- `cv2.namedWindow/imshow/waitKey` 仅在非 headless 下执行
|
|
||||||
|
|
||||||
- [ ] **Step 6: eval 脚本中在 headless 下跳过 `env.render()`**
|
|
||||||
|
|
||||||
只在 `eval.headless=false` 时调用 `env.render()`。
|
|
||||||
|
|
||||||
- [ ] **Step 7: 重新跑测试确认转绿**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_eval_vla_headless -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
### Task 5: 给训练脚本加可选 checkpoint-time rollout validation 薄封装
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/demos/vla_scripts/train_vla.py`
|
|
||||||
- Possibly modify: `roboimi/demos/vla_scripts/eval_vla.py`（仅当需要提取可复用入口）
|
|
||||||
- Modify: `roboimi/vla/conf/config.yaml`
|
|
||||||
- Test: `tests/test_train_vla_rollout_validation.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: 写 failing test，验证 checkpoint 保存点可选调用 rollout validation，且会传 `eval.headless=true`**
|
|
||||||
|
|
||||||
Test should:
|
|
||||||
- stub rollout/eval helper
|
|
||||||
- 开启 `train.rollout_validate_on_checkpoint=true`
|
|
||||||
- 设置小 `save_freq`
|
|
||||||
- 断言训练脚本在 checkpoint 时调用验证 helper
|
|
||||||
- 断言调用参数带 `headless=true`
|
|
||||||
|
|
||||||
- [ ] **Step 2: 跑测试确认它先失败**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_rollout_validation -v`
|
|
||||||
Expected: FAIL（当前无该 hook）
|
|
||||||
|
|
||||||
- [ ] **Step 3: 增加最小配置键**
|
|
||||||
|
|
||||||
在 `config.yaml` 中添加：
|
|
||||||
- `train.rollout_validate_on_checkpoint: false`
|
|
||||||
- `train.rollout_num_episodes: 1`
|
|
||||||
|
|
||||||
- [ ] **Step 4: 提取一个最小 rollout validation helper 接口**
|
|
||||||
|
|
||||||
要求：
|
|
||||||
- helper 输入至少包括 `cfg`, `ckpt_path`, `num_episodes`, `headless`
|
|
||||||
- 默认训练侧调用时强制 `headless=True`
|
|
||||||
- 优先复用现有 eval 逻辑，不引入第二套 validator 类
|
|
||||||
|
|
||||||
- [ ] **Step 5: 在 checkpoint 保存路径中接入 rollout helper**
|
|
||||||
|
|
||||||
要求：
|
|
||||||
- 不重写第二套验证框架
|
|
||||||
- 优先复用现有 eval 逻辑/工具
|
|
||||||
- 默认关闭
|
|
||||||
- 仅在 checkpoint 时少量调用
|
|
||||||
- 强制 `eval.headless=true`
|
|
||||||
|
|
||||||
- [ ] **Step 6: 重新跑测试确认转绿**
|
|
||||||
|
|
||||||
Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_rollout_validation -v`
|
|
||||||
Expected: PASS
|
|
||||||
|
|
||||||
### Task 6: 启动训练前的集成 smoke verification
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Verify only
|
|
||||||
|
|
||||||
- [ ] **Step 1: 跑所有新增/相关测试**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python -m unittest \
|
|
||||||
tests.test_calculate_stats_cli \
|
|
||||||
tests.test_train_vla_swanlab_logging \
|
|
||||||
tests.test_eval_vla_headless \
|
|
||||||
tests.test_train_vla_rollout_validation -v
|
|
||||||
```
|
|
||||||
Expected: 全部 PASS
|
|
||||||
|
|
||||||
- [ ] **Step 2: 对关键修改文件做语法检查**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python -m py_compile \
|
|
||||||
roboimi/vla/scripts/calculate_stats.py \
|
|
||||||
roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
roboimi/demos/vla_scripts/eval_vla.py \
|
|
||||||
roboimi/envs/double_pos_ctrl_env.py \
|
|
||||||
roboimi/envs/double_base.py
|
|
||||||
```
|
|
||||||
Expected: 无语法错误
|
|
||||||
|
|
||||||
- [ ] **Step 3: 运行训练 smoke run**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
SWANLAB_API_KEY='<user-provided>' \
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.max_steps=20 \
|
|
||||||
train.log_freq=1 \
|
|
||||||
train.save_freq=10 \
|
|
||||||
train.use_swanlab=true \
|
|
||||||
train.swanlab_project=roboimi-vla \
|
|
||||||
train.rollout_validate_on_checkpoint=false
|
|
||||||
```
|
|
||||||
Expected:
|
|
||||||
- 训练启动成功
|
|
||||||
- 产生 `checkpoints/vla_model_step_10.pt` 或 `vla_model_final.pt`
|
|
||||||
- 本地日志中无 ImportError
|
|
||||||
|
|
||||||
- [ ] **Step 4: 运行一个最小 headless rollout-validation smoke run**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
SWANLAB_API_KEY='<user-provided>' \
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.max_steps=10 \
|
|
||||||
train.log_freq=1 \
|
|
||||||
train.save_freq=5 \
|
|
||||||
train.use_swanlab=true \
|
|
||||||
train.swanlab_project=roboimi-vla \
|
|
||||||
train.rollout_validate_on_checkpoint=true \
|
|
||||||
train.rollout_num_episodes=1 \
|
|
||||||
eval.headless=true
|
|
||||||
```
|
|
||||||
Expected:
|
|
||||||
- 到达 checkpoint-time rollout 调用
|
|
||||||
- 不弹 MuJoCo viewer
|
|
||||||
- 不执行 `cv2.namedWindow/imshow/waitKey`
|
|
||||||
|
|
||||||
- [ ] **Step 5: 验证 checkpoint 文件已写出**
|
|
||||||
|
|
||||||
Run: `/usr/bin/zsh -lc 'ls -lah checkpoints | sed -n "1,120p"'`
|
|
||||||
Expected: 存在 `vla_model_step_10.pt` 或 `vla_model_final.pt`
|
|
||||||
|
|
||||||
- [ ] **Step 6: 验证 SwanLab 已收到标量**
|
|
||||||
|
|
||||||
验证方式：
|
|
||||||
- 终端日志中确认 `swanlab.init` / run URL / run id
|
|
||||||
- 若工具支持，确认 dashboard 中 `roboimi-vla` 项目下出现本次 run
|
|
||||||
|
|
||||||
### Task 7: 启动正式训练
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Verify only
|
|
||||||
|
|
||||||
- [ ] **Step 1: 用真实配置启动正式训练**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
```bash
|
|
||||||
SWANLAB_API_KEY='<user-provided>' \
|
|
||||||
/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
|
|
||||||
data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
|
|
||||||
train.use_swanlab=true \
|
|
||||||
train.swanlab_project=roboimi-vla \
|
|
||||||
train.rollout_validate_on_checkpoint=true \
|
|
||||||
eval.headless=true
|
|
||||||
```
|
|
||||||
Expected:
|
|
||||||
- 训练持续运行
|
|
||||||
- checkpoint 周期性写出
|
|
||||||
- SwanLab 周期性收到 train/val 标量
|
|
||||||
- 若 checkpoint-time rollout 打开，则不弹 GUI
|
|
||||||
|
|
||||||
- [ ] **Step 2: 记录启动信息并向用户汇报**
|
|
||||||
|
|
||||||
汇报内容至少包括：
|
|
||||||
- 使用的数据集路径
|
|
||||||
- 训练命令/关键 overrides
|
|
||||||
- checkpoint 输出目录
|
|
||||||
- SwanLab project 名称
|
|
||||||
- rollout validation 是否已启用以及是否 headless
|
|
||||||
@@ -1,311 +0,0 @@
|
|||||||
# sim_air_insert_ring_bar Implementation Plan
|
|
||||||
|
|
||||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
|
||||||
|
|
||||||
**Goal:** Add an independent dual-Diana MuJoCo task `sim_air_insert_ring_bar` with a square ring block, a square bar block, staged rewards, strict finite-geometry in-air insertion success detection, and a task-specific scripted policy.
|
|
||||||
|
|
||||||
**Architecture:** Reuse the current dual-Diana EE-control stack and environment factory, but add a task-specific scene XML, robot asset entrypoint, sampling helpers, and a new task-specific environment module. Keep `sim_transfer` untouched while introducing pure-Python geometry helpers and focused tests so reward/success behavior can be regression tested without requiring a full MuJoCo rollout in every test.
|
|
||||||
|
|
||||||
**Tech Stack:** Python, unittest, MuJoCo XML assets, existing dual-Diana environment classes, Hydra-compatible task naming/config patterns.
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
## File Structure / Responsibilities
|
|
||||||
|
|
||||||
- **Create:** `roboimi/assets/models/manipulators/DianaMed/ring_bar_objects.xml`
|
|
||||||
- Defines the rigid ring body and bar body, each with a free joint and stable box-based geoms.
|
|
||||||
- **Create:** `roboimi/assets/models/manipulators/DianaMed/bi_diana_ring_bar_ee.xml`
|
|
||||||
- Scene entrypoint that includes the shared world/table/robot assets plus the new object XML.
|
|
||||||
- **Modify:** `roboimi/assets/robots/diana_med.py`
|
|
||||||
- Add a task-specific robot asset class for the new scene XML without changing existing `BiDianaMed` behavior.
|
|
||||||
- **Modify:** `roboimi/utils/act_ex_utils.py`
|
|
||||||
- Add deterministic helpers to sample left/right planar placement regions for ring and bar objects.
|
|
||||||
- **Modify:** `roboimi/utils/constants.py`
|
|
||||||
- Register the new task name and default metadata.
|
|
||||||
- **Create:** `roboimi/envs/double_air_insert_env.py`
|
|
||||||
- New task-specific environment, finite-geometry success helpers, reset logic, reward logic, and task factory branch.
|
|
||||||
- **Modify:** `roboimi/envs/double_pos_ctrl_env.py`
|
|
||||||
- Route `make_sim_env()` to the new task-specific environment while keeping current `sim_transfer` logic unchanged.
|
|
||||||
- **Create:** `roboimi/demos/diana_air_insert_policy.py`
|
|
||||||
- Task-specific waypoint/open-loop scripted policy for grasp-lift-align-insert.
|
|
||||||
- **Modify:** `roboimi/demos/vla_scripts/eval_vla.py`
|
|
||||||
- Reset the new task with the correct sampled task state instead of assuming a single transfer box pose.
|
|
||||||
- **Create:** `tests/test_air_insert_env.py`
|
|
||||||
- Focused unit tests for sampling, reset helpers, reward progression, and strict success detection.
|
|
||||||
- **Modify:** `tests/test_eval_vla_headless.py`
|
|
||||||
- Add coverage that headless evaluation dispatches the correct reset sampler for the new task.
|
|
||||||
- **Modify:** `tests/test_robot_asset_paths.py`
|
|
||||||
- Verify the new robot asset class resolves its XML path correctly independent of cwd.
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 1: Add failing tests for task registration, samplers, and asset wiring
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Create: `tests/test_air_insert_env.py`
|
|
||||||
- Modify: `tests/test_eval_vla_headless.py`
|
|
||||||
- Modify: `tests/test_robot_asset_paths.py`
|
|
||||||
- Modify: `roboimi/utils/act_ex_utils.py` (later in implementation)
|
|
||||||
- Modify: `roboimi/utils/constants.py` (later in implementation)
|
|
||||||
- Modify: `roboimi/assets/robots/diana_med.py` (later in implementation)
|
|
||||||
- Modify: `roboimi/envs/double_pos_ctrl_env.py` (later in implementation)
|
|
||||||
- Create: `roboimi/envs/double_air_insert_env.py` (minimal stub in this task)
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write failing tests for task config and sampling helpers**
|
|
||||||
|
|
||||||
Add tests in `tests/test_air_insert_env.py` covering:
|
|
||||||
- `SIM_TASK_CONFIGS['sim_air_insert_ring_bar']` exists
|
|
||||||
- `sample_air_insert_ring_bar_pose()` (or equivalent helper) returns ring/bar positions with fixed z and correct left/right planar ranges
|
|
||||||
- output structure is explicit and easy for reset/eval code to consume
|
|
||||||
|
|
||||||
- [ ] **Step 2: Write failing tests for environment factory dispatch and robot asset resolution**
|
|
||||||
|
|
||||||
Add tests covering:
|
|
||||||
- `make_sim_env('sim_air_insert_ring_bar', headless=True)` dispatches to the new environment with rendering disabled
|
|
||||||
- a new robot asset class resolves the new XML path independent of cwd, similar to the existing `BiDianaMed` test pattern
|
|
||||||
|
|
||||||
- [ ] **Step 3: Write failing tests for eval reset helper dispatch**
|
|
||||||
|
|
||||||
Extend `tests/test_eval_vla_headless.py` so headless eval can reset the new task using the new sampler instead of hard-coding `sample_transfer_pose()`.
|
|
||||||
|
|
||||||
- [ ] **Step 4: Run the targeted tests to verify they fail for the expected missing-feature reasons**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env tests.test_eval_vla_headless tests.test_robot_asset_paths -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- FAIL because the new task config/helper/class/dispatch branch does not exist yet
|
|
||||||
|
|
||||||
- [ ] **Step 5: Implement the minimal production code to satisfy the new task registration and helper tests**
|
|
||||||
|
|
||||||
Implement only enough to make the new tests pass:
|
|
||||||
- add new task config entry
|
|
||||||
- add the new placement sampler
|
|
||||||
- add the new robot asset class
|
|
||||||
- create a minimal importable `double_air_insert_env.py` stub and class/function surface needed for factory dispatch tests
|
|
||||||
- add the factory dispatch branch / headless wiring
|
|
||||||
- update eval reset dispatch for the new task
|
|
||||||
|
|
||||||
- [ ] **Step 6: Re-run the targeted tests to verify they pass**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env tests.test_eval_vla_headless tests.test_robot_asset_paths -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS for the new registration/sampler/dispatch/asset tests
|
|
||||||
|
|
||||||
- [ ] **Step 7: Commit Task 1**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`git add tests/test_air_insert_env.py tests/test_eval_vla_headless.py tests/test_robot_asset_paths.py roboimi/utils/act_ex_utils.py roboimi/utils/constants.py roboimi/assets/robots/diana_med.py roboimi/envs/double_pos_ctrl_env.py roboimi/envs/double_air_insert_env.py roboimi/demos/vla_scripts/eval_vla.py && git commit -m "feat(env): register sim air insert ring bar task"`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 2: Add the MuJoCo ring+bar scene assets and reset helpers
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Create: `roboimi/assets/models/manipulators/DianaMed/ring_bar_objects.xml`
|
|
||||||
- Create: `roboimi/assets/models/manipulators/DianaMed/bi_diana_ring_bar_ee.xml`
|
|
||||||
- Create or Modify: `roboimi/envs/double_air_insert_env.py`
|
|
||||||
- Modify: `tests/test_air_insert_env.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write failing tests for object reset helpers and scene-specific joint naming assumptions**
|
|
||||||
|
|
||||||
In `tests/test_air_insert_env.py`, add unit tests for helper functions that:
|
|
||||||
- write ring pose to `ring_block_joint` from the named task-state mapping
|
|
||||||
- write bar pose to `bar_block_joint` from the named task-state mapping
|
|
||||||
- read back `env_state` as a stable 14D vector `[ring_pos, ring_quat, bar_pos, bar_quat]`
|
|
||||||
|
|
||||||
Use fake `mj_data` objects so tests stay fast and deterministic.
|
|
||||||
|
|
||||||
- [ ] **Step 2: Run the focused test slice and verify it fails**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- FAIL because reset/state helper functions and joint conventions are not implemented yet
|
|
||||||
|
|
||||||
- [ ] **Step 3: Implement the scene XML files and reset/state helper code**
|
|
||||||
|
|
||||||
Implement:
|
|
||||||
- the object XML with one rigid ring body and one rigid bar body
|
|
||||||
- the task scene XML entrypoint using the shared world/table/robot includes
|
|
||||||
- reset helper(s) in `double_air_insert_env.py` that set qpos for both free joints with fixed quaternions
|
|
||||||
- task-state accessor(s) returning both object poses in a stable structure
|
|
||||||
|
|
||||||
- [ ] **Step 4: Re-run the focused test slice and verify it passes**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS for reset/state helper tests
|
|
||||||
|
|
||||||
- [ ] **Step 5: Commit Task 2**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`git add roboimi/assets/models/manipulators/DianaMed/ring_bar_objects.xml roboimi/assets/models/manipulators/DianaMed/bi_diana_ring_bar_ee.xml roboimi/envs/double_air_insert_env.py tests/test_air_insert_env.py && git commit -m "feat(scene): add ring and bar insertion scene assets"`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 3: Implement strict reward and finite-geometry success detection
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/envs/double_air_insert_env.py`
|
|
||||||
- Modify: `tests/test_air_insert_env.py`
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write failing tests for reward stages and strict success detection**
|
|
||||||
|
|
||||||
Add tests in `tests/test_air_insert_env.py` for:
|
|
||||||
- left contact stage reward
|
|
||||||
- right contact stage reward
|
|
||||||
- ring lifted off table stage
|
|
||||||
- bar lifted off table stage
|
|
||||||
- positive success case where a finite bar truly passes through the aperture
|
|
||||||
- negative case where the centerline would pass but the finite square body would clip
|
|
||||||
- negative case where the bar has not crossed the ring thickness direction enough
|
|
||||||
- negative case where one/both objects are still on the table
|
|
||||||
|
|
||||||
Structure the tests around pure helper functions and light fake contact/state objects so the geometry logic is directly regression tested.
|
|
||||||
|
|
||||||
- [ ] **Step 2: Run the focused tests and verify they fail for missing reward/success logic**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- FAIL because the staged reward and finite-geometry insertion logic are not implemented yet
|
|
||||||
|
|
||||||
- [ ] **Step 3: Implement minimal strict success helpers and reward logic**
|
|
||||||
|
|
||||||
Implement in `roboimi/envs/double_air_insert_env.py`:
|
|
||||||
- pure helper(s) for transforming bar geometry into ring-local coordinates
|
|
||||||
- finite-geometry insertion predicate (not centerline-only)
|
|
||||||
- table-contact / airborne checks
|
|
||||||
- staged reward function returning the highest achieved stage with `max_reward = 5`
|
|
||||||
|
|
||||||
- [ ] **Step 4: Re-run the focused tests to verify the logic passes**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS for reward and success-detection regression tests
|
|
||||||
|
|
||||||
- [ ] **Step 5: Commit Task 3**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`git add roboimi/envs/double_air_insert_env.py tests/test_air_insert_env.py && git commit -m "feat(env): add strict air insertion reward and success logic"`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 4: Add the scripted policy and integration smoke coverage
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Create: `roboimi/demos/diana_air_insert_policy.py`
|
|
||||||
- Modify: `roboimi/demos/diana_record_sim_episodes.py`
|
|
||||||
- Modify: `tests/test_air_insert_env.py`
|
|
||||||
- Optionally Modify: `roboimi/demos/vla_scripts/eval_vla.py` (only if integration gaps remain after Task 1)
|
|
||||||
|
|
||||||
- [ ] **Step 1: Write failing tests for scripted-policy action shape and basic generation**
|
|
||||||
|
|
||||||
Add tests covering:
|
|
||||||
- the new policy produces a 16D action
|
|
||||||
- trajectory generation accepts sampled named task state without error
|
|
||||||
- the first action is a valid open-gripper safe pose command
|
|
||||||
- a deterministic nominal smoke path (with canonical sampled state or fake env shim) reaches the intended terminal interface contract without shape/reward mismatches
|
|
||||||
|
|
||||||
Keep the tests unit-level; do not require a full MuJoCo rollout for every assertion.
|
|
||||||
|
|
||||||
- [ ] **Step 2: Write failing tests for the scripted rollout entrypoint and a real headless smoke path**
|
|
||||||
|
|
||||||
Add coverage for both:
|
|
||||||
- the standard scripted rollout entrypoint (`roboimi/demos/diana_record_sim_episodes.py`) can select the new task sampler/policy instead of remaining sim_transfer-only
|
|
||||||
- a deterministic integration/smoke test that instantiates `make_sim_env('sim_air_insert_ring_bar', headless=True)`, resets with sampled named task state, and steps a few actions or scripted-policy outputs using the real task XML and task-specific wiring
|
|
||||||
|
|
||||||
- [ ] **Step 3: Run the scripted-policy tests and verify they fail**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- FAIL because the new scripted policy does not exist yet
|
|
||||||
|
|
||||||
- [ ] **Step 4: Implement the waypoint-based scripted policy**
|
|
||||||
|
|
||||||
Implement a conservative open-loop policy with phases:
|
|
||||||
- safe wait pose
|
|
||||||
- above-target approach
|
|
||||||
- descend + grasp
|
|
||||||
- dual lift
|
|
||||||
- airborne meeting alignment
|
|
||||||
- bar push-through insertion
|
|
||||||
|
|
||||||
Use fixed orientations for version 1 and follow the existing repository style from `diana_policy.py`.
|
|
||||||
|
|
||||||
- [ ] **Step 5: Re-run the scripted-policy tests to verify they pass**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS for scripted-policy tests
|
|
||||||
|
|
||||||
- [ ] **Step 6: Run the combined verification suite for this feature**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env tests.test_eval_vla_headless tests.test_eval_vla_rollout_artifacts tests.test_train_vla_rollout_validation tests.test_robot_asset_paths -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS with 0 failures
|
|
||||||
|
|
||||||
- [ ] **Step 6b: Run the mandatory real headless smoke check**
|
|
||||||
|
|
||||||
Run a focused smoke command that instantiates the real task, resets with sampled state, and steps a few actions using the new scripted policy or a deterministic action sequence.
|
|
||||||
|
|
||||||
Example command (adjust module/test helper if needed):
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env.AirInsertEnvSmokeTest -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS, proving the real XML/assets/env wiring instantiate and step correctly in headless mode
|
|
||||||
|
|
||||||
- [ ] **Step 7: Commit Task 4**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`git add roboimi/demos/diana_air_insert_policy.py tests/test_air_insert_env.py tests/test_eval_vla_headless.py tests/test_robot_asset_paths.py roboimi/demos/vla_scripts/eval_vla.py && git commit -m "feat(policy): add scripted air insertion policy"`
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 5: Final verification and implementation review
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Review all files touched above
|
|
||||||
|
|
||||||
- [ ] **Step 1: Run fresh end-to-end verification before claiming completion**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_air_insert_env tests.test_eval_vla_headless tests.test_robot_asset_paths -v`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- PASS with 0 failures
|
|
||||||
|
|
||||||
- [ ] **Step 2: Inspect git status and recent commits**
|
|
||||||
|
|
||||||
Run:
|
|
||||||
`git status --short && git log --oneline --decorate -n 8`
|
|
||||||
|
|
||||||
Expected:
|
|
||||||
- only intended feature files modified / committed
|
|
||||||
|
|
||||||
- [ ] **Step 3: Request final code review for the completed feature**
|
|
||||||
|
|
||||||
Use the requesting-code-review skill against the full diff from the feature branch starting point to current HEAD.
|
|
||||||
|
|
||||||
- [ ] **Step 4: Address any review findings and re-run verification if code changes**
|
|
||||||
|
|
||||||
If fixes are made, repeat the unittest command from Step 1.
|
|
||||||
|
|
||||||
- [ ] **Step 5: Hand off using finishing-a-development-branch**
|
|
||||||
|
|
||||||
After verification and review, use the finishing-a-development-branch skill to decide merge / PR / cleanup.
|
|
||||||
@@ -1,43 +0,0 @@
|
|||||||
# Held-out Episode Validation Implementation Plan
|
|
||||||
|
|
||||||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
|
||||||
|
|
||||||
**Goal:** Add optional held-out episode validation to main training flow, including explicit val episode selection and periodic action MSE evaluation without merging LEWM-only model features.
|
|
||||||
|
|
||||||
**Architecture:** Extend the generic dataset with optional episode filtering metadata, extend train config with explicit held-out validation knobs, and wire train_vla to choose between random split and explicit episode split. Reuse agent.predict_action_chunk for action MSE so the metric stays model-agnostic.
|
|
||||||
|
|
||||||
**Tech Stack:** Python, Hydra/OmegaConf, PyTorch, unittest
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
### Task 1: Add failing tests for dataset episode filtering and config semantics
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `tests/test_simple_robot_dataset_image_loading.py`
|
|
||||||
- Modify: `tests/test_train_vla_rollout_validation.py`
|
|
||||||
|
|
||||||
- [ ] Add dataset tests for `episode_indices` and `available_episode_indices`.
|
|
||||||
- [ ] Add training tests for explicit held-out episode splitting and fail-fast config validation.
|
|
||||||
- [ ] Run focused tests and verify they fail for the expected missing behavior.
|
|
||||||
|
|
||||||
### Task 2: Implement minimal dataset and training support
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Modify: `roboimi/vla/data/simpe_robot_dataset.py`
|
|
||||||
- Modify: `roboimi/vla/conf/config.yaml`
|
|
||||||
- Modify: `roboimi/demos/vla_scripts/train_vla.py`
|
|
||||||
|
|
||||||
- [ ] Add config keys `train.val_episode_indices` and `train.action_mse_val_freq_epochs`.
|
|
||||||
- [ ] Add optional dataset filtering by episode index plus `available_episode_indices` metadata.
|
|
||||||
- [ ] Add explicit train/val dataset builder and held-out action MSE computation.
|
|
||||||
- [ ] Log `val/action_mse` only when explicit held-out episode validation is configured.
|
|
||||||
|
|
||||||
### Task 3: Verify focused coverage
|
|
||||||
|
|
||||||
**Files:**
|
|
||||||
- Test: `tests/test_simple_robot_dataset_image_loading.py`
|
|
||||||
- Test: `tests/test_train_vla_rollout_validation.py`
|
|
||||||
- Test: `tests/test_train_vla_swanlab_logging.py`
|
|
||||||
|
|
||||||
- [ ] Run focused unittest targets for dataset filtering, held-out MSE, and SwanLab logging.
|
|
||||||
- [ ] Fix any regressions with minimal code changes.
|
|
||||||
@@ -1,242 +0,0 @@
|
|||||||
# VLA Experiment Sweep Design
|
|
||||||
|
|
||||||
## Goal
|
|
||||||
|
|
||||||
Run a matched-configuration training sweep on `/home/droid/project/diana_sim/sim_transfer` for the aligned Transformer diffusion policy, comparing:
|
|
||||||
|
|
||||||
1. ResNet ImageNet pretraining vs no pretraining under matched settings.
|
|
||||||
2. Transformer width/depth choices across a 3×3 grid.
|
|
||||||
|
|
||||||
The sweep should use the full dataset, headless rollout validation every 20 epochs, SwanLab logging, and a total budget of about 200 epochs per run.
|
|
||||||
|
|
||||||
## Current Context
|
|
||||||
|
|
||||||
- Dataset: `100` episodes, `70000` frame-level samples.
|
|
||||||
- Cameras used as image conditions: `r_vis`, `top`, `front`.
|
|
||||||
- Current policy family: ResNet visual encoder + Transformer DDPM head.
|
|
||||||
- Current training path already supports:
|
|
||||||
- full-dataset training (`val_split=0.0`)
|
|
||||||
- checkpoint-time and epoch-time rollout hooks
|
|
||||||
- headless eval path
|
|
||||||
- SwanLab logging
|
|
||||||
- best-checkpoint selection by rollout reward once rollout metrics exist
|
|
||||||
- Current trainer behavior that matters for execution:
|
|
||||||
- step checkpoints are written to `checkpoints/` relative to the Hydra job working directory
|
|
||||||
- epoch-time rollout validation is controlled by `train.rollout_val_freq_epochs`
|
|
||||||
- checkpoint-time rollout validation is separately controlled by `train.rollout_validate_on_checkpoint`
|
|
||||||
- per-run log files, pid files, and run directories must be created by the external launcher, not by the trainer itself
|
|
||||||
|
|
||||||
## Derived Schedule
|
|
||||||
|
|
||||||
For a chosen global batch size `B`, define:
|
|
||||||
|
|
||||||
- `steps_per_epoch = floor(70000 / B)` because training currently uses `drop_last=True` when the train set is larger than the batch.
|
|
||||||
- `rollout_freq_steps = 20 * steps_per_epoch`
|
|
||||||
- `max_steps = 200 * steps_per_epoch`
|
|
||||||
|
|
||||||
Example at `B = 32`:
|
|
||||||
|
|
||||||
- `steps_per_epoch = 2187`
|
|
||||||
- `rollout_freq_steps = 43740`
|
|
||||||
- `max_steps = 437400`
|
|
||||||
|
|
||||||
This schedule must be recomputed after the batch-size probe chooses the final shared batch size.
|
|
||||||
|
|
||||||
## Experiment Strategy
|
|
||||||
|
|
||||||
### Recommended approach
|
|
||||||
|
|
||||||
Use one **shared batch size across all runs**, chosen by probing the largest model that will appear in the sweep. This preserves fairness while still using as much GPU memory as safely available.
|
|
||||||
|
|
||||||
### Why not per-run max batch size
|
|
||||||
|
|
||||||
Allowing each model to use a different batch size would add another changing variable, making the 11-run comparison harder to interpret.
|
|
||||||
|
|
||||||
### Unique executions vs logical comparisons
|
|
||||||
|
|
||||||
There are `11` logical comparison points in the user request (`2` for the pretraining comparison and `9` for the emb/layer grid), but only `10` **unique** training configurations because:
|
|
||||||
|
|
||||||
- the pretrained baseline in Section A (`pretrained_backbone_weights=IMAGENET1K_V1`, `n_emb=128`, `n_layer=4`) is also one cell of the Section B sweep.
|
|
||||||
|
|
||||||
This design will therefore run **10 unique training jobs**, and the pretrained-on baseline will be reused as the `(128, 4)` architecture-sweep point. This avoids wasting one full 200-epoch run on an identical configuration.
|
|
||||||
|
|
||||||
The reused baseline has one canonical identity throughout execution:
|
|
||||||
|
|
||||||
- run name stem: `sim-transfer-baseline-pretrain-on-emb128-layer4`
|
|
||||||
- this same run is counted both as:
|
|
||||||
- the pretrained-on arm of the 2-point pretraining comparison
|
|
||||||
- the `(n_emb=128, n_layer=4)` point of the 9-point architecture sweep
|
|
||||||
|
|
||||||
## Batch-Size Probe
|
|
||||||
|
|
||||||
Before launching the full sweep:
|
|
||||||
|
|
||||||
1. Probe the largest planned model:
|
|
||||||
- `n_emb=384`
|
|
||||||
- `n_layer=12`
|
|
||||||
- ResNet pretrained enabled
|
|
||||||
2. Try candidate batch sizes in ascending order:
|
|
||||||
- `32`
|
|
||||||
- `48`
|
|
||||||
- `64`
|
|
||||||
- optionally `80` if `64` is clearly safe
|
|
||||||
3. For each candidate, run a short GPU smoke training with:
|
|
||||||
- real dataset
|
|
||||||
- three cameras
|
|
||||||
- no SwanLab
|
|
||||||
- no rollout validation
|
|
||||||
- `max_steps=4`
|
|
||||||
4. A candidate **passes** if all 4 steps finish without:
|
|
||||||
- CUDA OOM
|
|
||||||
- process crash / abort
|
|
||||||
- NaN / Inf loss
|
|
||||||
5. Choose the **highest passing** candidate and use it for all sweep runs.
|
|
||||||
|
|
||||||
Learning rate should scale linearly using the current training rule:
|
|
||||||
|
|
||||||
- config baseline: `lr=1e-4` at `batch_size=16`
|
|
||||||
- equivalent rule: `lr = 1e-4 * (batch_size / 16)`
|
|
||||||
- equivalently: `lr = 2e-4 * (batch_size / 32)`
|
|
||||||
|
|
||||||
## Experiment Matrix
|
|
||||||
|
|
||||||
Total logical comparison points: `11`
|
|
||||||
|
|
||||||
Total unique training executions: `10`
|
|
||||||
|
|
||||||
### A. ResNet pretraining comparison (2 runs)
|
|
||||||
|
|
||||||
Fixed Transformer settings:
|
|
||||||
|
|
||||||
- `n_emb=128`
|
|
||||||
- `n_layer=4`
|
|
||||||
- `n_head=4`
|
|
||||||
- `freeze_backbone=true`
|
|
||||||
- `use_separate_rgb_encoder_per_camera=true`
|
|
||||||
|
|
||||||
Interpretation note:
|
|
||||||
|
|
||||||
- This comparison keeps the current frozen-backbone recipe unchanged.
|
|
||||||
- Therefore, the `pretrained_backbone_weights=null` run measures **random frozen visual features vs ImageNet frozen visual features**.
|
|
||||||
- It does **not** test end-to-end training of the ResNet from scratch; that would be a separate experiment.
|
|
||||||
|
|
||||||
Runs:
|
|
||||||
|
|
||||||
1. `pretrained_backbone_weights=IMAGENET1K_V1`
|
|
||||||
2. `pretrained_backbone_weights=null`
|
|
||||||
|
|
||||||
### B. Transformer architecture sweep (9 runs)
|
|
||||||
|
|
||||||
Fixed visual backbone setting:
|
|
||||||
|
|
||||||
- `pretrained_backbone_weights=IMAGENET1K_V1`
|
|
||||||
- `freeze_backbone=true`
|
|
||||||
- `use_separate_rgb_encoder_per_camera=true`
|
|
||||||
- `n_head=4`
|
|
||||||
|
|
||||||
Rationale:
|
|
||||||
|
|
||||||
- The 9-point emb/layer sweep fixes `pretrained_backbone_weights=IMAGENET1K_V1`.
|
|
||||||
- The separate 2-run comparison in Section A isolates the effect of ResNet pretraining at the baseline architecture.
|
|
||||||
|
|
||||||
Grid:
|
|
||||||
|
|
||||||
- `n_emb ∈ {128, 256, 384}`
|
|
||||||
- `n_layer ∈ {4, 8, 12}`
|
|
||||||
|
|
||||||
## Shared Training Configuration
|
|
||||||
|
|
||||||
All full runs should use:
|
|
||||||
|
|
||||||
- dataset: `/home/droid/project/diana_sim/sim_transfer`
|
|
||||||
- cameras: `[r_vis, top, front]`
|
|
||||||
- device: `cuda`
|
|
||||||
- `val_split=0.0`
|
|
||||||
- `num_workers=12`
|
|
||||||
- `seed=42`
|
|
||||||
- headless rollout validation
|
|
||||||
- `rollout_val_freq_epochs=20`
|
|
||||||
- `rollout_validate_on_checkpoint=false`
|
|
||||||
- `rollout_num_episodes=3`
|
|
||||||
- `use_swanlab=true`
|
|
||||||
- shared batch size from probe
|
|
||||||
- shared learning-rate rule from probe
|
|
||||||
- `max_steps` recomputed to represent about `200` epochs
|
|
||||||
|
|
||||||
Reproducibility note:
|
|
||||||
|
|
||||||
- `train.seed=42` will be fixed and recorded for all runs.
|
|
||||||
- Under the current `train_vla.py`, this does **not** make the runs bitwise deterministic when `val_split=0.0`, because model initialization and shuffled training order are not fully seeded by the trainer today.
|
|
||||||
- The sweep is therefore treated as a **matched-config comparison**, not a strict deterministic benchmark.
|
|
||||||
|
|
||||||
## Rollout / Validation Policy
|
|
||||||
|
|
||||||
- No held-out val split will be used.
|
|
||||||
- Loss remains a fallback best-model metric until the first rollout reward exists.
|
|
||||||
- After rollout metrics begin, the best checkpoint should be chosen by **higher average rollout reward**.
|
|
||||||
- Rollout should run with:
|
|
||||||
- no GUI
|
|
||||||
- CPU eval device
|
|
||||||
- current evaluation helper path
|
|
||||||
|
|
||||||
## Execution Model
|
|
||||||
|
|
||||||
Runs should be launched **serially**, not concurrently, because:
|
|
||||||
|
|
||||||
- the user currently has one GPU available
|
|
||||||
- concurrent jobs would reduce throughput and complicate memory management
|
|
||||||
- serial execution preserves clean logs and easier resume semantics
|
|
||||||
|
|
||||||
Each run gets:
|
|
||||||
|
|
||||||
- a dedicated run directory under `runs/`
|
|
||||||
- its own log file
|
|
||||||
- its own pid / launcher metadata if backgrounded
|
|
||||||
- a unique SwanLab run name
|
|
||||||
- `hydra.job.chdir=true`
|
|
||||||
- `hydra.run.dir=<run_dir>` so the trainer-local `checkpoints/` land inside the run directory
|
|
||||||
|
|
||||||
## Naming Convention
|
|
||||||
|
|
||||||
Suggested run name pattern:
|
|
||||||
|
|
||||||
- pretraining comparison:
|
|
||||||
- `sim-transfer-baseline-pretrain-on-emb128-layer4-bs{B}-lr{LR}-e200`
|
|
||||||
- `sim-transfer-pretrain-off-bs{B}-lr{LR}-e200`
|
|
||||||
- architecture sweep:
|
|
||||||
- `(128,4)` reuses `sim-transfer-baseline-pretrain-on-emb128-layer4-bs{B}-lr{LR}-e200`
|
|
||||||
- `sim-transfer-emb{E}-layer{L}-bs{B}-lr{LR}-e200`
|
|
||||||
|
|
||||||
## Success Criteria
|
|
||||||
|
|
||||||
The sweep is successful when:
|
|
||||||
|
|
||||||
1. A shared safe batch size has been found.
|
|
||||||
2. All 10 unique runs have launchable commands and isolated run directories.
|
|
||||||
3. Each run trains for about 200 epochs worth of steps.
|
|
||||||
4. Rollout validation occurs every 20 epochs.
|
|
||||||
5. SwanLab records train metrics and rollout rewards.
|
|
||||||
6. Final comparison can be made using best rollout average reward and training traces across:
|
|
||||||
- 2-point pretraining comparison
|
|
||||||
- 9-point emb/layer sweep
|
|
||||||
|
|
||||||
## Non-Goals
|
|
||||||
|
|
||||||
- No change to dataset format.
|
|
||||||
- No change to model architecture beyond the requested pretraining toggle and Transformer width/depth sweep.
|
|
||||||
- No attempt to equalize wall-clock time across models.
|
|
||||||
- No multi-GPU scheduling.
|
|
||||||
|
|
||||||
## Risks and Mitigations
|
|
||||||
|
|
||||||
### Risk: larger models may OOM
|
|
||||||
|
|
||||||
Mitigation: probe the largest model first and choose one shared safe batch size.
|
|
||||||
|
|
||||||
### Risk: long runs may be interrupted
|
|
||||||
|
|
||||||
Mitigation: use per-run directories, periodic checkpoints, and resumable launch commands.
|
|
||||||
|
|
||||||
### Risk: rollout validation increases total training time
|
|
||||||
|
|
||||||
Mitigation: keep rollout headless, CPU-based, and limited to 3 episodes every 20 epochs.
|
|
||||||
@@ -1,316 +0,0 @@
|
|||||||
# sim_air_insert_ring_bar Design
|
|
||||||
|
|
||||||
## Summary
|
|
||||||
|
|
||||||
Add a new independent MuJoCo simulation task named `sim_air_insert_ring_bar` that keeps the existing dual-Diana tabletop setup but replaces the single transfer box with two randomized objects:
|
|
||||||
|
|
||||||
- a square ring block grasped by the left arm
|
|
||||||
- a square bar block grasped by the right arm
|
|
||||||
|
|
||||||
The task is to pick both objects off the table and complete an in-air insertion where the bar truly passes through the ring aperture. The existing `sim_transfer` task must remain unchanged.
|
|
||||||
|
|
||||||
## Goals
|
|
||||||
|
|
||||||
- Reuse the current dual-Diana EE-control simulation stack
|
|
||||||
- Keep the same table/base robot arrangement as the existing transfer task
|
|
||||||
- Add an independent task entrypoint and scene definition
|
|
||||||
- Randomize planar placement of both objects within left/right task-specific regions
|
|
||||||
- Implement reward staging for contact, lift, and successful in-air insertion
|
|
||||||
- Add a scripted policy that performs pick, lift, align, and in-air insertion
|
|
||||||
- Preserve compatibility with existing environment creation, evaluation, and rollout patterns
|
|
||||||
|
|
||||||
## Non-Goals
|
|
||||||
|
|
||||||
- No random yaw in the first version
|
|
||||||
- No visual servoing or closed-loop insertion controller
|
|
||||||
- No general multi-task environment framework refactor
|
|
||||||
- No guarantee that the VLA training stack is immediately tuned for this new task
|
|
||||||
- No replacement or behavior change for `sim_transfer`
|
|
||||||
|
|
||||||
## Task Name
|
|
||||||
|
|
||||||
Use a new task name:
|
|
||||||
|
|
||||||
- `sim_air_insert_ring_bar`
|
|
||||||
|
|
||||||
This task should be exposed alongside `sim_transfer`, not as a replacement.
|
|
||||||
|
|
||||||
## Scene Geometry
|
|
||||||
|
|
||||||
### Shared Base Scene
|
|
||||||
|
|
||||||
Keep the dual Diana robot, the table, and the existing camera layout conceptually unchanged.
|
|
||||||
|
|
||||||
### Ring Block
|
|
||||||
|
|
||||||
Represent the square ring as a rigid free body composed from simple MuJoCo box geoms rather than an external mesh.
|
|
||||||
|
|
||||||
Dimensions:
|
|
||||||
|
|
||||||
- outer side length: 68 mm
|
|
||||||
- inner aperture side length: 32 mm
|
|
||||||
- thickness: 18 mm
|
|
||||||
- ring wall width: 18 mm
|
|
||||||
|
|
||||||
The ring should behave as a single object body with a single free joint.
|
|
||||||
|
|
||||||
### Bar Block
|
|
||||||
|
|
||||||
Represent the bar as a rigid free body with a single box geom.
|
|
||||||
|
|
||||||
Dimensions:
|
|
||||||
|
|
||||||
- length: 90 mm
|
|
||||||
- cross-section: 18 mm x 18 mm
|
|
||||||
|
|
||||||
The bar should also be a single free-joint body.
|
|
||||||
|
|
||||||
## Initial Placement / Reset
|
|
||||||
|
|
||||||
The first version uses position-only randomization with fixed orientation. Reset sampling stays **caller-driven**, matching the existing `sim_transfer` usage pattern in rollout/eval code: a helper samples task state, then callers pass that state into `env.reset(...)`.
|
|
||||||
|
|
||||||
Use an explicit sampled task-state structure with named fields:
|
|
||||||
|
|
||||||
- `ring_pos`: 3D position
|
|
||||||
- `ring_quat`: fixed 4D quaternion for version 1
|
|
||||||
- `bar_pos`: 3D position
|
|
||||||
- `bar_quat`: fixed 4D quaternion for version 1
|
|
||||||
|
|
||||||
Behavior:
|
|
||||||
|
|
||||||
- ring block: randomized only in a left-side planar sampling region
|
|
||||||
- bar block: randomized only in a right-side planar sampling region
|
|
||||||
- both objects start flat on the table
|
|
||||||
- both objects use fixed orientation at reset
|
|
||||||
- no random yaw, tilt, or flip in this version
|
|
||||||
|
|
||||||
The sampling regions should be chosen conservatively so that:
|
|
||||||
|
|
||||||
- the left arm can comfortably reach and grasp the ring
|
|
||||||
- the right arm can comfortably reach and grasp the bar
|
|
||||||
- scripted open-loop pick trajectories remain feasible
|
|
||||||
|
|
||||||
## Control / Action Interface
|
|
||||||
|
|
||||||
Reuse the current 16D EE-space action convention already used by the dual-Diana position-control environment:
|
|
||||||
|
|
||||||
- left arm EE pose: 7D (`xyz + quat`)
|
|
||||||
- right arm EE pose: 7D (`xyz + quat`)
|
|
||||||
- left gripper command: 1D
|
|
||||||
- right gripper command: 1D
|
|
||||||
|
|
||||||
The new task should continue using EE targets transformed through the existing IK-based control path.
|
|
||||||
|
|
||||||
## Environment Structure
|
|
||||||
|
|
||||||
Implement this as a new task-specific environment path while reusing the existing dual-Diana simulation base where possible.
|
|
||||||
|
|
||||||
Expected responsibilities:
|
|
||||||
|
|
||||||
- scene instantiation for the ring+bar setup
|
|
||||||
- task reset for randomized object placement
|
|
||||||
- environment-state accessors for both objects
|
|
||||||
- reward computation
|
|
||||||
- in-air insertion success detection
|
|
||||||
|
|
||||||
The environment factory must dispatch by task name and leave the `sim_transfer` branch unchanged.
|
|
||||||
|
|
||||||
## Observation / Environment State
|
|
||||||
|
|
||||||
The task should retain the current observation structure style used by the dual-Diana environment:
|
|
||||||
|
|
||||||
- `qpos`
|
|
||||||
- multi-camera images
|
|
||||||
|
|
||||||
For task state access, the environment should expose a stable `env_state` vector with this exact order:
|
|
||||||
|
|
||||||
- `ring_pos[0:3]`
|
|
||||||
- `ring_quat[3:7]`
|
|
||||||
- `bar_pos[7:10]`
|
|
||||||
- `bar_quat[10:14]`
|
|
||||||
|
|
||||||
This 14D state should be sufficient for scripted-policy debugging and future rollout analysis, while reset itself remains caller-driven via the named task-state helper structure above.
|
|
||||||
|
|
||||||
## Reward Design
|
|
||||||
|
|
||||||
Use staged rewards in the same spirit as the current task, returning the highest achieved stage rather than accumulating one-time sparse bonuses per event.
|
|
||||||
|
|
||||||
Maximum reward:
|
|
||||||
|
|
||||||
- `max_reward = 5`
|
|
||||||
|
|
||||||
Reward stages:
|
|
||||||
|
|
||||||
1. left gripper touches the ring block
|
|
||||||
2. right gripper touches the bar block
|
|
||||||
3. ring block is lifted off the table
|
|
||||||
4. bar block is lifted off the table
|
|
||||||
5. while both objects are off the table, the bar truly passes through the ring aperture
|
|
||||||
|
|
||||||
Notes:
|
|
||||||
|
|
||||||
- contact rewards are intended as grasp-progress stages
|
|
||||||
- lift rewards require the object to be off the table, not merely touched
|
|
||||||
- final success reward only applies when both objects are airborne
|
|
||||||
|
|
||||||
## Success Detection
|
|
||||||
|
|
||||||
Success must **not** be based on a centerline-only check.
|
|
||||||
|
|
||||||
A centerline-only test is insufficient because:
|
|
||||||
|
|
||||||
- the bar has thickness, so a centerline can pass through while the body cannot
|
|
||||||
- a square bar with imperfect orientation can have its centerline inside the aperture while its corners still collide with the ring
|
|
||||||
|
|
||||||
### Required Success Semantics
|
|
||||||
|
|
||||||
A successful insertion requires all of the following:
|
|
||||||
|
|
||||||
1. the ring is off the table
|
|
||||||
2. the bar is off the table
|
|
||||||
3. the bar has actually crossed through the ring thickness direction
|
|
||||||
4. the bar’s finite square cross-section fits through the square aperture during that crossing
|
|
||||||
|
|
||||||
### Recommended Detection Approach
|
|
||||||
|
|
||||||
Use a task-level geometric check in Python rather than relying on contact alone.
|
|
||||||
|
|
||||||
Implementation intent:
|
|
||||||
|
|
||||||
- transform the bar geometry into the ring’s local frame
|
|
||||||
- reason about the bar as a finite oriented box (not a line)
|
|
||||||
- verify that the bar has crossed the ring thickness direction
|
|
||||||
- verify that the portion of the bar passing the aperture fits within the inner square opening, accounting for the bar’s cross-section and orientation
|
|
||||||
|
|
||||||
This geometric check is the primary success test.
|
|
||||||
|
|
||||||
### Role of Contacts
|
|
||||||
|
|
||||||
Contacts may still be used for:
|
|
||||||
|
|
||||||
- grasp-stage rewards
|
|
||||||
- debugging / diagnostics
|
|
||||||
|
|
||||||
But contact alone should **not** be the sole criterion for insertion success, since:
|
|
||||||
|
|
||||||
- a true clean insertion may have limited aperture-wall contact
|
|
||||||
- persistent contact can also happen while the bar is jammed and not actually inserted
|
|
||||||
|
|
||||||
## Scripted Policy
|
|
||||||
|
|
||||||
Add a new task-specific scripted policy for `sim_air_insert_ring_bar`.
|
|
||||||
|
|
||||||
### Policy Intent
|
|
||||||
|
|
||||||
The first version prioritizes a conservative, reliable open-loop demonstration rather than an optimized trajectory.
|
|
||||||
|
|
||||||
### Action Phases
|
|
||||||
|
|
||||||
The scripted policy should follow these phases:
|
|
||||||
|
|
||||||
1. move both arms to safe initial / waiting poses with grippers open
|
|
||||||
2. move left arm above the ring and right arm above the bar
|
|
||||||
3. descend and grasp the assigned objects
|
|
||||||
4. lift both objects clear of the table
|
|
||||||
5. move both objects to an airborne meeting region above the table
|
|
||||||
6. hold the ring stably while aligning the bar with the aperture
|
|
||||||
7. push the bar along the intended insertion direction until the geometric success condition is met
|
|
||||||
|
|
||||||
### Grasp Assignment
|
|
||||||
|
|
||||||
- left arm: ring only
|
|
||||||
- right arm: bar only
|
|
||||||
|
|
||||||
### Motion Style
|
|
||||||
|
|
||||||
Keep the current repository style:
|
|
||||||
|
|
||||||
- waypoint-based trajectory definition
|
|
||||||
- open-loop interpolation between waypoints
|
|
||||||
- fixed grasp orientation in the first version
|
|
||||||
|
|
||||||
No adaptive replanning is required for the first version.
|
|
||||||
|
|
||||||
## Files / Integration Scope
|
|
||||||
|
|
||||||
The implementation is expected to add task-specific files rather than broadly refactoring the codebase.
|
|
||||||
|
|
||||||
Likely additions / changes:
|
|
||||||
|
|
||||||
- a new MuJoCo scene XML for the ring+bar task
|
|
||||||
- one or more XML fragments defining the two new objects
|
|
||||||
- a new task-specific dual-Diana environment file
|
|
||||||
- robot asset wiring for the new scene XML
|
|
||||||
- reset sampling helpers for the new task
|
|
||||||
- task registration in constants / environment factory paths
|
|
||||||
- a new scripted policy file
|
|
||||||
- focused tests for task creation, reset, rewards, success detection, and scripted policy shape/smoke behavior
|
|
||||||
|
|
||||||
## Testing Requirements
|
|
||||||
|
|
||||||
At minimum, add regression coverage for:
|
|
||||||
|
|
||||||
### Environment Creation
|
|
||||||
|
|
||||||
- the new task can be created via the task factory
|
|
||||||
- the existing `sim_transfer` task remains unchanged
|
|
||||||
|
|
||||||
### Reset / Sampling
|
|
||||||
|
|
||||||
- ring reset positions are inside the left sampling region
|
|
||||||
- bar reset positions are inside the right sampling region
|
|
||||||
- reset orientation is fixed as intended
|
|
||||||
|
|
||||||
### Environment State
|
|
||||||
|
|
||||||
- environment-state access returns both object poses in the expected structure
|
|
||||||
|
|
||||||
### Success Detection
|
|
||||||
|
|
||||||
Must include both positive and negative cases.
|
|
||||||
|
|
||||||
Positive case:
|
|
||||||
|
|
||||||
- a configuration where the finite bar truly passes through the ring aperture is detected as success
|
|
||||||
|
|
||||||
Negative cases:
|
|
||||||
|
|
||||||
- centerline-inside but finite body would clip the aperture
|
|
||||||
- not enough depth / not actually crossing the ring thickness direction
|
|
||||||
- one or both objects still on the table
|
|
||||||
|
|
||||||
### Reward Logic
|
|
||||||
|
|
||||||
- left contact stage
|
|
||||||
- right contact stage
|
|
||||||
- ring lift stage
|
|
||||||
- bar lift stage
|
|
||||||
- final success stage with `max_reward = 5`
|
|
||||||
|
|
||||||
### Scripted Policy
|
|
||||||
|
|
||||||
At minimum:
|
|
||||||
|
|
||||||
- policy emits valid 16D actions
|
|
||||||
- trajectory generation does not error
|
|
||||||
- rollout smoke path can step through the new environment
|
|
||||||
|
|
||||||
## Risks / Constraints
|
|
||||||
|
|
||||||
- MuJoCo contact naming must remain stable enough for stage rewards
|
|
||||||
- geometric insertion checks must be strict enough to avoid false positives but not so brittle that numerically valid insertions are missed
|
|
||||||
- scripted open-loop insertion may require conservative alignment and lift heights to keep the first version reliable
|
|
||||||
|
|
||||||
## Acceptance Criteria
|
|
||||||
|
|
||||||
The feature is complete when all of the following are true:
|
|
||||||
|
|
||||||
- `sim_air_insert_ring_bar` is creatable as an independent task
|
|
||||||
- the scene contains the dual Diana, table, ring block, and bar block
|
|
||||||
- reset randomizes ring and bar positions in left/right planar regions with fixed orientation
|
|
||||||
- the environment exposes task state for both objects
|
|
||||||
- staged rewards progress to `max_reward = 5`
|
|
||||||
- final success is based on finite-geometry insertion semantics, not a centerline-only shortcut
|
|
||||||
- a new scripted policy can execute the intended pick-lift-align-insert behavior in the new environment
|
|
||||||
- a canonical nominal smoke path (unit-level or deterministic integration-level) exists for the new scripted-policy interface so success is not judged purely by interpretation
|
|
||||||
- existing `sim_transfer` behavior is preserved
|
|
||||||
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|
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@@ -1,216 +0,0 @@
|
|||||||
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|
|
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|
|
||||||
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|
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|
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"std": [
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},
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"cream_cheese_1": {
|
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"min": [
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"max": [
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"range": [
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},
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"wooden_cabinet_1": {
|
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"min": [
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"max": [
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"range": [
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"std": [
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||||||
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|
|
||||||
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|
|
||||||
"wine_rack_1": {
|
|
||||||
"min": [
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|
||||||
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||||||
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||||||
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|
||||||
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|
|
||||||
"eef_pos": {
|
|
||||||
"min": [
|
|
||||||
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|
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|
|
||||||
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|
|
||||||
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|
|
||||||
"max": [
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|
||||||
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|
|
||||||
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|
|
||||||
"range": [
|
|
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||||||
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|
|
||||||
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|
|
||||||
"joint_pos": {
|
|
||||||
"min": [
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||||||
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||||||
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|
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||||||
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|
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||||||
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|
|
||||||
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|
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||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"max": [
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|
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|
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||||||
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|
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|
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||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
"range": [
|
|
||||||
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|
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||||||
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|
|
||||||
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|
|
||||||
0.110617,
|
|
||||||
0.019791,
|
|
||||||
0.088656,
|
|
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|
|
||||||
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|
|
||||||
"std": [
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||||||
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|
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||||||
0.021384,
|
|
||||||
0.004568,
|
|
||||||
0.020009,
|
|
||||||
0.017693
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@@ -1,552 +0,0 @@
|
|||||||
[
|
|
||||||
{
|
|
||||||
"init_id": 0,
|
|
||||||
"akita_black_bowl_1": {
|
|
||||||
"pos": [
|
|
||||||
-0.097626,
|
|
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"rollout_response_timeout_s": 300.0,
|
|
||||||
"rollout_server_startup_timeout_s": 300.0,
|
|
||||||
"warmup_steps": 2000,
|
|
||||||
"scheduler_type": "cosine",
|
|
||||||
"min_lr": 1e-06,
|
|
||||||
"weight_decay": 1e-05,
|
|
||||||
"grad_clip": 1.0,
|
|
||||||
"pretrained_ckpt": null
|
|
||||||
},
|
|
||||||
"experiment": {
|
|
||||||
"name": "vla_diffusion",
|
|
||||||
"notes": "",
|
|
||||||
"tags": []
|
|
||||||
}
|
|
||||||
}
|
|
||||||
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@@ -1,200 +0,0 @@
|
|||||||
import mujoco
|
|
||||||
from mujoco import viewer
|
|
||||||
import sys
|
|
||||||
import numpy as np
|
|
||||||
import time
|
|
||||||
import threading
|
|
||||||
|
|
||||||
|
|
||||||
class MjBasicRenderer:
|
|
||||||
def __new__(cls, *args, **kwargs):
|
|
||||||
return super().__new__(cls)
|
|
||||||
|
|
||||||
def __init__(self, mj_model=None, mj_data=None):
|
|
||||||
# keyboard flag
|
|
||||||
self.render_paused = True
|
|
||||||
self.exit_flag = False
|
|
||||||
# init param
|
|
||||||
self.mj_model = mj_model
|
|
||||||
self.mj_data = mj_data
|
|
||||||
self.renderer = "viewer" # default
|
|
||||||
self.viewer = None
|
|
||||||
self._image = None
|
|
||||||
|
|
||||||
# Set up mujoco viewer
|
|
||||||
self.image_renderer = mujoco.Renderer(self.mj_model)
|
|
||||||
|
|
||||||
def __del__(self):
|
|
||||||
pass
|
|
||||||
|
|
||||||
def _init_renderer(self):
|
|
||||||
"""Initialize renderer, choose official renderer with "viewer"(joined from version 2.3.3),
|
|
||||||
another renderer with "mujoco_viewer"
|
|
||||||
"""
|
|
||||||
|
|
||||||
def key_callback(keycode):
|
|
||||||
if keycode == 32: # space
|
|
||||||
self.render_paused = not self.render_paused
|
|
||||||
elif keycode == 256: # escape
|
|
||||||
self.exit_flag = not self.exit_flag
|
|
||||||
|
|
||||||
if self.renderer == "viewer":
|
|
||||||
# This function does not block, allowing user code to continue execution.
|
|
||||||
self.viewer = viewer.launch_passive(
|
|
||||||
self.mj_model,
|
|
||||||
self.mj_data,
|
|
||||||
key_callback=key_callback,
|
|
||||||
show_left_ui=False,
|
|
||||||
show_right_ui=False,
|
|
||||||
)
|
|
||||||
self.set_renderer_config()
|
|
||||||
else:
|
|
||||||
raise ValueError("Invalid renderer for some reason.")
|
|
||||||
|
|
||||||
def render(self):
|
|
||||||
"""mujoco render"""
|
|
||||||
if self.viewer is not None and self.render_paused is True:
|
|
||||||
if self.viewer.is_running() and self.exit_flag is False:
|
|
||||||
self.viewer: viewer.Handle
|
|
||||||
self.viewer.sync()
|
|
||||||
else:
|
|
||||||
self.viewer.close()
|
|
||||||
|
|
||||||
def set_renderer_config(self):
|
|
||||||
"""Setup mujoco global config while using viewer as renderer.
|
|
||||||
It should be noted that the render thread need locked.
|
|
||||||
"""
|
|
||||||
self.viewer.cam.lookat = np.array([0.4, 0, 0.5])
|
|
||||||
self.viewer.cam.azimuth -= 0.005
|
|
||||||
with self.viewer.lock():
|
|
||||||
self.viewer.opt.flags[mujoco.mjtVisFlag.mjVIS_CONTACTPOINT] = int(
|
|
||||||
self.mj_data.time % 2
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
try:
|
|
||||||
import cv2
|
|
||||||
except ImportError:
|
|
||||||
print("Could not import cv2, please install it to enable camera viewer.")
|
|
||||||
|
|
||||||
|
|
||||||
class MjMultiRenderer(MjBasicRenderer):
|
|
||||||
|
|
||||||
# __slots__=('mj_model','mj_data','renderer','enable_camera_viewer')
|
|
||||||
def __new__(cls, *args, **kwargs):
|
|
||||||
return super().__new__(cls)
|
|
||||||
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
mj_model=None,
|
|
||||||
mj_data=None,
|
|
||||||
renderer=None,
|
|
||||||
enable_camera_viewer=False,
|
|
||||||
enable_depth=False,
|
|
||||||
):
|
|
||||||
super().__init__(mj_model, mj_data)
|
|
||||||
self._depth = None
|
|
||||||
self.renderer = renderer
|
|
||||||
self._init_renderer()
|
|
||||||
self.enable_camera_viewer = enable_camera_viewer
|
|
||||||
if self.enable_camera_viewer:
|
|
||||||
self.enable_depth = enable_depth
|
|
||||||
self._init_window()
|
|
||||||
else:
|
|
||||||
self.enable_depth = False
|
|
||||||
print("No Camera View")
|
|
||||||
|
|
||||||
def __del__(self):
|
|
||||||
self.close()
|
|
||||||
|
|
||||||
def _init_renderer(self):
|
|
||||||
"""
|
|
||||||
Initialize renderer, choose official renderer with "viewer"(joined from version 2.3.3)
|
|
||||||
"""
|
|
||||||
if self.renderer == "unity":
|
|
||||||
# TODO: Support unity renderer.
|
|
||||||
raise ValueError("Unity renderer init failed for no supporting reason")
|
|
||||||
elif self.renderer == "viewer":
|
|
||||||
super()._init_renderer()
|
|
||||||
print("mujoco viewer init !")
|
|
||||||
else:
|
|
||||||
raise ValueError("renderer init failed for some reason.")
|
|
||||||
|
|
||||||
def _init_window(self, name="Camera view"):
|
|
||||||
if not self.enable_depth:
|
|
||||||
cv2.namedWindow(name, cv2.WINDOW_NORMAL)
|
|
||||||
else:
|
|
||||||
cv2.namedWindow(name, cv2.WINDOW_NORMAL)
|
|
||||||
cv2.namedWindow("Camera depth view", cv2.WINDOW_NORMAL)
|
|
||||||
|
|
||||||
def render(self):
|
|
||||||
"""render mujoco"""
|
|
||||||
if self.renderer == "viewer":
|
|
||||||
super().render()
|
|
||||||
elif self.renderer == "unity":
|
|
||||||
# TODO: Support unity renderer.
|
|
||||||
raise ValueError("Unity renderer not supported now.")
|
|
||||||
else:
|
|
||||||
raise ValueError("Invalid renderer for some reason.")
|
|
||||||
|
|
||||||
def render(self):
|
|
||||||
"""mujoco render"""
|
|
||||||
if self.viewer is not None and self.render_paused is True:
|
|
||||||
if self.viewer.is_running() and self.exit_flag is False:
|
|
||||||
self.viewer: viewer.Handle
|
|
||||||
self.viewer.sync()
|
|
||||||
else:
|
|
||||||
self.viewer.close()
|
|
||||||
|
|
||||||
def camera_render(self, cam=None):
|
|
||||||
if self.enable_camera_viewer:
|
|
||||||
if not self.enable_depth:
|
|
||||||
rgb, depth = self.render_from_camera(cam)
|
|
||||||
rgb = cv2.resize(rgb, (1920, 1600))
|
|
||||||
cv2.imshow("Camera view", rgb)
|
|
||||||
cv2.waitKey(1)
|
|
||||||
|
|
||||||
else:
|
|
||||||
rgb, depth = self.render_from_camera(cam)
|
|
||||||
cv2.imshow("Camera view", rgb)
|
|
||||||
cv2.imshow("Camera depth view", depth)
|
|
||||||
cv2.waitKey(1)
|
|
||||||
|
|
||||||
else:
|
|
||||||
print("camera info disable")
|
|
||||||
return
|
|
||||||
|
|
||||||
def render_from_camera(self, cam=None):
|
|
||||||
self.image_renderer.update_scene(self.mj_data, camera=cam)
|
|
||||||
if self.enable_depth is True:
|
|
||||||
self.image_renderer.enable_depth_rendering()
|
|
||||||
org = self.image_renderer.render()
|
|
||||||
depth = org[:, :]
|
|
||||||
self.image_renderer.disable_depth_rendering()
|
|
||||||
org = self.image_renderer.render()
|
|
||||||
image = org[:, :, ::-1]
|
|
||||||
else:
|
|
||||||
org = self.image_renderer.render()
|
|
||||||
image = org[:, :, ::-1]
|
|
||||||
depth = np.zeros([240, 320])
|
|
||||||
return image, depth
|
|
||||||
|
|
||||||
def close(self):
|
|
||||||
"""close the environment."""
|
|
||||||
if self.enable_camera_viewer and self.viewer.is_running() == False:
|
|
||||||
cv2.destroyAllWindows()
|
|
||||||
self.viewer.close()
|
|
||||||
# sys.exit(0)
|
|
||||||
|
|
||||||
# def get_cam_intrinsic(self, fovy=45.0, width=320, height=240):
|
|
||||||
# aspect = width * 1.0 / height
|
|
||||||
# fovx = np.degrees(2 * np.arctan(aspect * np.tan(np.radians(fovy / 2))))
|
|
||||||
|
|
||||||
# cx = 0.5 * width
|
|
||||||
# cy = 0.5 * height
|
|
||||||
# fx = cx / np.tan(fovx * np.pi / 180 * 0.5)
|
|
||||||
# fy = cy / np.tan(fovy * np.pi / 180 * 0.5)
|
|
||||||
|
|
||||||
# K = np.array([[fx, 0, cx],
|
|
||||||
# [0, fy, cy],
|
|
||||||
# [0, 0, 1]], dtype=np.float32)
|
|
||||||
@@ -76,7 +76,7 @@
|
|||||||
<body name="ee_cam_left" pos="0.00 0.046 -0.075" euler="0.0 0.0 -0.0">
|
<body name="ee_cam_left" pos="0.00 0.046 -0.075" euler="0.0 0.0 -0.0">
|
||||||
<inertial pos="0 0 0" quat="1 0 0 0" mass="0" diaginertia="0 0 0" />
|
<inertial pos="0 0 0" quat="1 0 0 0" mass="0" diaginertia="0 0 0" />
|
||||||
<geom type="mesh" contype="1" conaffinity="1" group="1" rgba="0.69804 0.69804 0.69804 1" mesh="realsense_cam" />
|
<geom type="mesh" contype="1" conaffinity="1" group="1" rgba="0.69804 0.69804 0.69804 1" mesh="realsense_cam" />
|
||||||
<camera name="rs_cam_left" mode="fixed" pos="0.0 0.0 0.01" euler="0.0 9.4 0.0 " fovy="50" resolution="1920 1200"/>
|
<camera name="rs_cam_left" mode="fixed" pos="0.0 0.0 -0.25" euler="0.0 9.4 0.0 " fovy="15" resolution="1920 1200"/>
|
||||||
</body>
|
</body>
|
||||||
</body>
|
</body>
|
||||||
<body name="l_finger_left" pos="0 0.01 0.0444">
|
<body name="l_finger_left" pos="0 0.01 0.0444">
|
||||||
|
|||||||
@@ -1,6 +0,0 @@
|
|||||||
<mujoco model="bi_diana_socket_peg">
|
|
||||||
<include file="./empty_world.xml" />
|
|
||||||
<include file="./table_square.xml" />
|
|
||||||
<include file="./socket_peg_objects.xml" />
|
|
||||||
<include file="./BiDianaMed_rethink.xml" />
|
|
||||||
</mujoco>
|
|
||||||
@@ -1,19 +0,0 @@
|
|||||||
<mujoco model="socket_peg_objects">
|
|
||||||
<worldbody>
|
|
||||||
<body name="peg" pos="0.12 0.90 0.46">
|
|
||||||
<joint name="red_peg_joint" type="free" frictionloss="0.01" />
|
|
||||||
<inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
|
|
||||||
<geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.06 0.01 0.01" type="box" name="red_peg" rgba="1 0 0 1" />
|
|
||||||
</body>
|
|
||||||
|
|
||||||
<body name="socket" pos="-0.12 0.90 0.472">
|
|
||||||
<joint name="blue_socket_joint" type="free" frictionloss="0.01" />
|
|
||||||
<inertial pos="0 0 0" mass="0.05" diaginertia="0.002 0.002 0.002" />
|
|
||||||
<geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0 -0.02" size="0.06 0.018 0.002" type="box" name="socket-1" rgba="0 0 1 1" />
|
|
||||||
<geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0 0.02" size="0.06 0.018 0.002" type="box" name="socket-2" rgba="0 0 1 1" />
|
|
||||||
<geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 0.02 0" size="0.06 0.002 0.018" type="box" name="socket-3" rgba="0 0 1 1" />
|
|
||||||
<geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.05 0.001" pos="0 -0.02 0" size="0.06 0.002 0.018" type="box" name="socket-4" rgba="0 0 1 1" />
|
|
||||||
<geom condim="4" solimp="2 1 0.01" solref="0.01 1" friction="1 0.005 0.0001" pos="0 0 0" size="0.04 0.01 0.01" type="box" name="pin" rgba="1 0 0 1" />
|
|
||||||
</body>
|
|
||||||
</worldbody>
|
|
||||||
</mujoco>
|
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user