# PLAN

## Goal
Train a 50k-step IMF baseline with the original ResNet vision backbone, using only the `front` camera as image conditioning.

## Fixed comparison contract
- Same as the active `top/front` run except image input is reduced to `[front]`
- Agent: `resnet_imf_attnres`
- Vision backbone mode: `resnet`
- `pred_horizon=16`, `num_action_steps=8`
- `n_emb=384`, `n_layer=12`, `n_head=1`, `n_kv_head=1`
- `inference_steps=1`
- `batch_size=80`, `lr=2.5e-4`, cosine, warmup=2000
- dataset: `/home/droid/sim_dataset/sim_transfer`
- cameras: `[front]` only
- rollout every 5 epochs with 5 episodes, headless

## Resource plan
- Host: `100.119.99.14`
- GPU: `0`

## Important dimension override
- Single-camera visual cond dim = `64 + 16 = 80`, so override `agent.head.cond_dim=80` and `agent.num_cams=1`.

## Execution path
1. 2-step smoke test on remote GPU0.
2. If smoke passes, launch 50k main run with SwanLab.
3. Record pid / run_dir / log / URL locally.