# RoboCasa365

[RoboCasa365](https://robocasa.ai) is a large-scale simulation framework for training and benchmarking **generalist robots** in everyday kitchen tasks. It ships 365 diverse manipulation tasks across 2,500 kitchen environments, 3,200+ object assets and 600+ hours of human demonstration data, on a PandaOmron 12-DOF mobile manipulator (Franka arm on a holonomic base).

- Paper: [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://arxiv.org/abs/2406.02523)
- GitHub: [robocasa/robocasa](https://github.com/robocasa/robocasa)
- Project website: [robocasa.ai](https://robocasa.ai)
- Pretrained policy: [`lerobot/smolvla_robocasa`](https://huggingface.co/lerobot/smolvla_robocasa)
- Single-task dataset (CloseFridge): [`pepijn223/robocasa_CloseFridge`](https://huggingface.co/datasets/pepijn223/robocasa_CloseFridge)

<img
  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/robocasa-banner.webp"
  alt="RoboCasa365 benchmark overview"
  width="85%"
/>

## Available tasks

RoboCasa365 organizes its 365 tasks into two families and three upstream benchmark groups that LeRobot exposes as first-class `--env.task` shortcuts:

| Family    | Tasks | Description                                                                     |
| --------- | ----- | ------------------------------------------------------------------------------- |
| Atomic    | ~65   | Single-skill tasks: pick-and-place, door/drawer manipulation, appliance control |
| Composite | ~300  | Multi-step tasks across 60+ categories: cooking, cleaning, organizing, etc.     |

**Atomic task examples:** `CloseFridge`, `OpenDrawer`, `OpenCabinet`, `TurnOnMicrowave`, `TurnOffStove`, `NavigateKitchen`, `PickPlaceCounterToStove`.

**Composite task categories:** baking, boiling, brewing, chopping, clearing table, defrosting food, loading dishwasher, making tea, microwaving food, washing dishes, and more.

`--env.task` accepts three forms:

- a single task name (`CloseFridge`)
- a comma-separated list (`CloseFridge,OpenBlenderLid,PickPlaceCoffee`)
- 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`).

## Installation

RoboCasa and its dependency `robosuite` are not published on PyPI, and RoboCasa's own `setup.py` hardcodes `lerobot==0.3.3`, which conflicts with this repo's `lerobot`. LeRobot therefore does **not** expose a `robocasa` extra — install the two packages manually as editable clones (using `--no-deps` on `robocasa` to skip its shadowed `lerobot` pin):

```bash
# After following the standard LeRobot installation instructions.

git clone https://github.com/robocasa/robocasa.git ~/robocasa
git clone https://github.com/ARISE-Initiative/robosuite.git ~/robosuite
pip install -e ~/robocasa --no-deps
pip install -e ~/robosuite

# Robocasa's runtime deps (the ones its setup.py would have pulled, minus
# the bad lerobot pin).
pip install numpy numba scipy mujoco pygame Pillow opencv-python \
            pyyaml pynput tqdm termcolor imageio h5py lxml hidapi \
            tianshou gymnasium

python -m robocasa.scripts.setup_macros
# Lightweight assets (lightwheel object meshes + textures). Enough for
# the default env out of the box.
python -m robocasa.scripts.download_kitchen_assets \
  --type tex tex_generative fixtures_lw objs_lw
# Optional: full objaverse/aigen registries (~30GB) for richer object
# variety. Enable at eval time via --env.obj_registries (see below).
# python -m robocasa.scripts.download_kitchen_assets --type objs_objaverse
```

RoboCasa requires MuJoCo. Set the rendering backend before training or evaluation:

```bash
export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
```

### Object registries

By default the env samples objects only from the `lightwheel` registry (what `--type objs_lw` ships), which avoids a `Probabilities contain NaN` crash when the objaverse / aigen packs aren't on disk. If you've downloaded the full asset set, enable the full registry at runtime:

```bash
--env.obj_registries='[objaverse,lightwheel]'
```

## Evaluation

All eval snippets below mirror the CI command (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps RoboCasa's native camera keys (`robot0_agentview_left` / `robot0_eye_in_hand` / `robot0_agentview_right`) onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_robocasa` policy was trained on.

### Single-task evaluation (recommended for quick iteration)

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_robocasa \
  --env.type=robocasa \
  --env.task=CloseFridge \
  --eval.batch_size=1 \
  --eval.n_episodes=20 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
```

### Multi-task evaluation

Pass a comma-separated list of tasks:

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_robocasa \
  --env.type=robocasa \
  --env.task=CloseFridge,OpenCabinet,OpenDrawer,TurnOnMicrowave,TurnOffStove \
  --eval.batch_size=1 \
  --eval.n_episodes=20 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
```

### Benchmark-group evaluation

Run an entire upstream group (e.g. all 18 `atomic_seen` tasks with `split=target`):

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_robocasa \
  --env.type=robocasa \
  --env.task=atomic_seen \
  --eval.batch_size=1 \
  --eval.n_episodes=20 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  '--rename_map={"observation.images.robot0_agentview_left": "observation.images.camera1", "observation.images.robot0_eye_in_hand": "observation.images.camera2", "observation.images.robot0_agentview_right": "observation.images.camera3"}'
```

### Recommended evaluation episodes

**20 episodes per task** for reproducible benchmarking. Matches the protocol used in published results.

## Policy inputs and outputs

**Observations** (raw RoboCasa camera names are preserved verbatim):

- `observation.state` — 16-dim proprioceptive state (base position, base quaternion, relative end-effector position, relative end-effector quaternion, gripper qpos)
- `observation.images.robot0_agentview_left` — left agent view, 256×256 HWC uint8
- `observation.images.robot0_eye_in_hand` — wrist camera view, 256×256 HWC uint8
- `observation.images.robot0_agentview_right` — right agent view, 256×256 HWC uint8

**Actions:**

- Continuous control in `Box(-1, 1, shape=(12,))` — base motion (4D) + control mode (1D) + end-effector position (3D) + end-effector rotation (3D) + gripper (1D).

## Training

### Single-task example

A ready-to-use single-task dataset is on the Hub:
[`pepijn223/robocasa_CloseFridge`](https://huggingface.co/datasets/pepijn223/robocasa_CloseFridge).

Fine-tune a SmolVLA base on `CloseFridge`:

```bash
lerobot-train \
  --policy.type=smolvla \
  --policy.repo_id=${HF_USER}/smolvla_robocasa_CloseFridge \
  --policy.load_vlm_weights=true \
  --policy.push_to_hub=true \
  --dataset.repo_id=pepijn223/robocasa_CloseFridge \
  --env.type=robocasa \
  --env.task=CloseFridge \
  --output_dir=./outputs/smolvla_robocasa_CloseFridge \
  --steps=100000 \
  --batch_size=4 \
  --env_eval_freq=5000 \
  --eval.batch_size=1 \
  --eval.n_episodes=5 \
  --save_freq=10000
```

Evaluate the resulting checkpoint:

```bash
lerobot-eval \
  --policy.path=${HF_USER}/smolvla_robocasa_CloseFridge \
  --env.type=robocasa \
  --env.task=CloseFridge \
  --eval.batch_size=1 \
  --eval.n_episodes=20
```

## Reproducing published results

The released checkpoint [`lerobot/smolvla_robocasa`](https://huggingface.co/lerobot/smolvla_robocasa) is evaluated with the commands in the [Evaluation](#evaluation) section. CI runs a 10-atomic-task smoke eval (one episode each) on every PR touching the benchmark, picking fixture-centric tasks that don't require the objaverse asset pack.