Repository Guidelines

Project Structure & Module Organization

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/.
tests/ holds unittest regressions for training, rollout, dataset loading, and asset resolution.
docs/superpowers/ stores design specs and plans.
docs/experiment-guide.md store how beginning experiment
runs/, outputs/, swanlog/, .worktrees/, and checkpoints/ are generated experiment artifacts, not source modules.

conda env create -f environment.yml && conda activate roboimi — create the standard environment.
pip install -e . — editable install.
python -m unittest discover -s tests -p 'test_*.py' — run all tests.
python -m unittest tests.test_train_vla_rollout_validation -v — run one focused test module.
python roboimi/vla/scripts/calculate_stats.py — regenerate normalization stats.
python roboimi/demos/vla_scripts/train_vla.py train.max_steps=1000 — quick training smoke test.
python roboimi/demos/vla_scripts/eval_vla.py eval.ckpt_path=checkpoints/vla_model_best.pt eval.num_episodes=5 — evaluate a checkpoint.

Use Python, 4-space indentation, and PEP 8–style naming.
Prefer snake_case for files, functions, variables, and Hydra keys; use CamelCase for classes.
Match existing Hydra namespaces such as train.*, eval.*, and agent.*.
Keep diffs focused and imports tidy. Experiment names usually encode host/GPU, e.g. ...-l20g3-..., ...-5880g1-..., or ...-5090-....

Tests use unittest and unittest.mock.
Name files tests/test_*.py and classes *Test.
Add direct regression coverage for changes to training, rollout, config, or dataset code paths.
No strict coverage target is defined, but changed behavior should be tested and affected suites should pass before review.

Follow existing prefixes: feat, fix, chore, docs, debug, optionally with scope, e.g. feat(eval): export rollout video timing.
Keep commit messages imperative and specific.
PRs should state the goal, touched configs/scripts, exact test commands, and dataset/checkpoint assumptions. Attach visual artifacts only when outputs change.

Local workstation (droid-z790eagleax) — local shell; 1× RTX 5090 32 GB; env: /home/droid/.conda/envs/roboimi/bin/python.
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.
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.