- envs(robocasa): hoist the duplicated `_parse_camera_names` helper
out of `libero.py` and `robocasa.py` into `envs/utils.py` as the
public `parse_camera_names`; call sites updated.
- envs(robocasa): give each factory a distinct `episode_index`
(`0..n_envs-1`) and derive a per-worker seed series in `reset()`
so n_envs workers don't all roll the same scene under a shared
outer seed.
- envs(robocasa): drop the unused `**kwargs` on `_make_env`; declare
`visualization_height` / `visualization_width` on both the wrapper
and the `RoboCasaEnv` config + propagate via `gym_kwargs`.
- envs(robocasa): emit `info["final_info"]` on termination (matching
MetaWorld) so downstream vector-env auto-reset keeps the terminal
task/success flags.
- docs(robocasa): add `--rename_map` (robot0_agentview_left/
eye_in_hand/agentview_right → camera1/2/3) plus CI-parity flags to
all three eval snippets.
- docker(robocasa): pin robocasa + robosuite git SHAs and the pip
dep versions (pygame, Pillow, opencv-python, pyyaml, pynput, tqdm,
termcolor, imageio, h5py, lxml, hidapi, gymnasium) for
reproducible benchmark images.
- ci(robocasa): update the workflow comment — there is no
`lerobot[robocasa]` extra; robocasa/robosuite are installed
manually because upstream's `lerobot==0.3.3` pin shadows ours.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Rework docs/source/robocasa.mdx to follow the standard benchmark doc
structure: intro + links + available tasks (with family breakdown and
first-class benchmark-group shortcuts) + installation + eval +
recommended episodes + policy I/O + training + reproducing results.
- Fix the paper link (was pointing at a non-existent arxiv ID).
- Surface lerobot/smolvla_robocasa and pepijn223/robocasa_CloseFridge
in the top-of-page links so they're findable without reading the
training section.
- Add an explicit "Object registries" subsection explaining the
`--env.obj_registries=[objaverse,lightwheel]` override path.
- Add an explicit "Reproducing published results" section pointing
at the CI smoke eval.
Made-with: Cursor
robocasa's upstream setup.py hardcodes `lerobot==0.3.3` in
install_requires. Exposing it as the `lerobot[robocasa]` extra made
uv's dep resolver cycle: `lerobot[robocasa]` -> robocasa -> lerobot
(a different version) -> unsolvable. This broke every `uv sync` — even
invocations with an unrelated extra like `--extra test` — because uv
validates the whole lockfile graph.
- Remove the `robocasa` extra from pyproject.toml. Installation
instructions in docs/source/robocasa.mdx now walk users through the
manual `git clone` + `pip install --no-deps` flow, which matches
what the Docker image already does and sidesteps the cyclic dep
entirely.
- Dockerfile: `uv pip install -e ~/robocasa --no-deps` so the
shadowed lerobot==0.3.3 never lands in the image; install
robocasa's actual runtime deps (numpy, numba, scipy, mujoco,
tianshou, etc.) explicitly.
Made-with: Cursor
CloseFridge (and most kitchen tasks) crashed at reset with
`ValueError: Probabilities contain NaN` coming out of
`sample_kitchen_object_helper`. RoboCasa's upstream default
`obj_registries=("objaverse", "lightwheel")` normalizes per-registry
candidate counts as probabilities; when a sampled category has zero
mjcf paths in every configured registry (because the objaverse asset
pack isn't on disk — ~30GB, skipped by our Docker build), the 0/0
divide yields NaNs and `rng.choice` raises.
- Add `obj_registries: list[str] = ["lightwheel"]` to `RoboCasaEnv`
config; thread it through `create_robocasa_envs`, `_make_env_fns`,
and the gym.Env wrapper to the underlying `RoboCasaGymEnv` (which
forwards to `create_env` → `robosuite.make` → kitchen env).
- Default matches what `download_kitchen_assets --type objs_lw`
actually ships, so the env works out of the box without a 30GB
objaverse download.
- Document the override (`--env.obj_registries='[objaverse,lightwheel]'`)
for users who have downloaded the full asset set.
Made-with: Cursor
- Rewrite observation section to use raw RoboCasa camera keys
(observation.images.robot0_agentview_{left,right},
observation.images.robot0_eye_in_hand).
- Add a "Training on a single task" section with a full smolvla
training command on pepijn223/robocasa_CloseFridge, plus matching
single-task eval command.
- Document benchmark-group task shortcuts (atomic_seen, composite_seen,
composite_unseen, pretrain50/100/200/300) as valid --env.task values.
Made-with: Cursor
Pepijn