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Pepijn
81ee2952a3
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
138 lines
5.3 KiB
Plaintext
138 lines
5.3 KiB
Plaintext
# RoboCasa365
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RoboCasa365 is a large-scale simulation framework for training and benchmarking **generalist robots** in everyday kitchen tasks. It provides 365 diverse manipulation tasks across 2,500 kitchen environments, with over 3,200 object assets and 600+ hours of human demonstration data. The benchmark tests whether robots can handle the diversity and complexity of real-world household environments.
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- Paper: [RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots](https://arxiv.org/abs/2603.04356)
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- GitHub: [robocasa/robocasa](https://github.com/robocasa/robocasa)
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- Project website: [robocasa.ai](https://robocasa.ai)
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## Available tasks
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RoboCasa365 includes **365 tasks** organized into atomic (single-skill) and composite (multi-step) categories:
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| Category | Tasks | Description |
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| --------- | ----- | ------------------------------------------------------------------------------- |
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| Atomic | ~65 | Single-skill tasks: pick-and-place, door/drawer manipulation, appliance control |
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| Composite | ~300 | Multi-step tasks across 60+ categories: cooking, cleaning, organizing, etc. |
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**Atomic task examples:** `CloseFridge`, `OpenBlenderLid`, `PickPlaceCoffee`, `ManipulateStoveKnob`, `NavigateKitchen`, `TurnOnToaster`
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**Composite task categories:** baking, boiling, brewing, chopping food, clearing table, defrosting food, loading dishwasher, making tea, microwaving food, washing dishes, and many more.
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`--env.task` accepts three forms:
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- a single task name (e.g. `CloseFridge`)
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- a comma-separated list of task names (e.g. `CloseFridge,OpenBlenderLid`)
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- a benchmark-group shortcut — `atomic_seen`, `composite_seen`, `composite_unseen`, `pretrain50`, `pretrain100`, `pretrain200`, `pretrain300` — which auto-expands to the upstream task list and auto-sets the dataset `split` (`target` or `pretrain`).
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## Installation
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After following the LeRobot installation instructions:
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```bash
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pip install -e ".[robocasa]"
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python -m robocasa.scripts.setup_macros
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# Lightweight assets only (lightwheel registry, ~2GB). Enough for the
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# default env out of the box.
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python -m robocasa.scripts.download_kitchen_assets --type tex fixtures_lw objs_lw
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# Optional: full objaverse/aigen registries (~30GB) for richer object
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# variety. If you download these, pass them via `--env.obj_registries`
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# at eval/train time (see below).
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# python -m robocasa.scripts.download_kitchen_assets --type objaverse
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```
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<Tip>
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RoboCasa365 requires MuJoCo for simulation. Set the rendering backend before training or evaluation:
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```bash
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export MUJOCO_GL=egl # for headless servers (HPC, cloud)
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```
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</Tip>
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By default the env samples objects only from the `lightwheel` registry (the one shipped by `--type objs_lw`), which avoids a `Probabilities contain NaN` crash when the objaverse/aigen packs are absent. If you've downloaded the full asset set, enable it with:
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```bash
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--env.obj_registries='[objaverse,lightwheel]'
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```
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## Policy inputs and outputs
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**Observations** (raw RoboCasa camera names are preserved verbatim):
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- `observation.state` -- 16-dim proprioceptive state (base position, base quaternion, relative end-effector position, relative end-effector quaternion, gripper qpos)
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- `observation.images.robot0_agentview_left` -- left agent view, 256x256 HWC uint8
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- `observation.images.robot0_eye_in_hand` -- wrist camera view, 256x256 HWC uint8
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- `observation.images.robot0_agentview_right` -- right agent view, 256x256 HWC uint8
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**Actions:**
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- Continuous control in `Box(-1, 1, shape=(12,))` -- base motion (4D) + control mode (1D) + end-effector position (3D) + end-effector rotation (3D) + gripper (1D)
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## Training on a single task
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A ready-to-use single-task dataset is available on the Hub:
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[`pepijn223/robocasa_CloseFridge`](https://huggingface.co/datasets/pepijn223/robocasa_CloseFridge).
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Train a SmolVLA policy on `CloseFridge`:
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```bash
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lerobot-train \
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--policy.type=smolvla \
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--policy.repo_id=${HF_USER}/smolvla_robocasa_CloseFridge \
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--policy.load_vlm_weights=true \
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--policy.push_to_hub=true \
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--dataset.repo_id=pepijn223/robocasa_CloseFridge \
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--env.type=robocasa \
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--env.task=CloseFridge \
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--output_dir=./outputs/smolvla_robocasa_CloseFridge \
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--steps=100000 \
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--batch_size=4 \
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--eval_freq=5000 \
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--eval.batch_size=1 \
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--eval.n_episodes=5 \
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--save_freq=10000
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```
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Evaluate the trained checkpoint on the same task:
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```bash
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lerobot-eval \
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--policy.path=${HF_USER}/smolvla_robocasa_CloseFridge \
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--env.type=robocasa \
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--env.task=CloseFridge \
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--eval.batch_size=1 \
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--eval.n_episodes=20
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```
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## Multi-task evaluation
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Evaluate across several tasks at once:
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```bash
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lerobot-eval \
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--policy.path=your-policy-id \
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--env.type=robocasa \
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--env.task=CloseFridge,OpenBlenderLid,PickPlaceCoffee \
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--eval.batch_size=1 \
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--eval.n_episodes=20
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```
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Or run a full benchmark group:
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```bash
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lerobot-eval \
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--policy.path=your-policy-id \
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--env.type=robocasa \
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--env.task=atomic_seen \
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--eval.batch_size=1 \
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--eval.n_episodes=20
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```
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- `--eval.batch_size` controls how many environments run in parallel.
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- `--eval.n_episodes` sets how many episodes to run per task.
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### Recommended evaluation episodes
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For reproducible benchmarking, use **20 episodes per task**. This matches the protocol used in published results.
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