# RoboCasa365

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.

- Paper: [RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots](https://arxiv.org/abs/2603.04356)
- GitHub: [robocasa/robocasa](https://github.com/robocasa/robocasa)
- Project website: [robocasa.ai](https://robocasa.ai)

## Available tasks

RoboCasa365 includes **365 tasks** organized into atomic (single-skill) and composite (multi-step) categories:

| Category  | 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`, `OpenBlenderLid`, `PickPlaceCoffee`, `ManipulateStoveKnob`, `NavigateKitchen`, `TurnOnToaster`

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

Pass individual task class names directly as `--env.task` (e.g., `CloseFridge`, `PickPlaceCoffee`). Multiple tasks can be comma-separated for multi-task evaluation.

## Installation

After following the LeRobot installation instructions:

```bash
pip install -e ".[robocasa]"
python -m robocasa.scripts.setup_macros
python -m robocasa.scripts.download_kitchen_assets
```

<Tip>
RoboCasa365 requires MuJoCo for simulation. Set the rendering backend before training or evaluation:

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

</Tip>

## Evaluation

### Single-task evaluation

Evaluate a policy on a single RoboCasa task:

```bash
lerobot-eval \
  --policy.path="your-policy-id" \
  --env.type=robocasa \
  --env.task=CloseFridge \
  --eval.batch_size=1 \
  --eval.n_episodes=20
```

### Multi-task evaluation

Evaluate across multiple tasks at once by passing a comma-separated list:

```bash
lerobot-eval \
  --policy.path="your-policy-id" \
  --env.type=robocasa \
  --env.task=CloseFridge,OpenBlenderLid,PickPlaceCoffee \
  --eval.batch_size=1 \
  --eval.n_episodes=20
```

- `--env.task` accepts individual task names (comma-separated).
- `--eval.batch_size` controls how many environments run in parallel.
- `--eval.n_episodes` sets how many episodes to run per task.

### Policy inputs and outputs

**Observations:**

- `observation.state` -- 16-dim proprioceptive state (base position, base quaternion, relative end-effector position, relative end-effector quaternion, gripper qpos)
- `observation.images.image` -- left agent view (`robot0_agentview_left`), 256x256 HWC uint8
- `observation.images.image2` -- wrist camera view (`robot0_eye_in_hand`), 256x256 HWC uint8
- `observation.images.image3` -- right agent view (`robot0_agentview_right`), 256x256 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)

### Recommended evaluation episodes

For reproducible benchmarking, use **20 episodes per task**. This matches the protocol used in published results.

## Training

### Example training command

```bash
lerobot-train \
  --policy.type=smolvla \
  --policy.repo_id=${HF_USER}/robocasa-test \
  --policy.load_vlm_weights=true \
  --dataset.repo_id=your-robocasa-dataset \
  --env.type=robocasa \
  --env.task=CloseFridge \
  --output_dir=./outputs/ \
  --steps=100000 \
  --batch_size=4 \
  --eval.batch_size=1 \
  --eval.n_episodes=1 \
  --eval_freq=1000
```