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Add RoboCasa365 (arXiv:2603.04356) as a new simulation benchmark with 365 everyday kitchen manipulation tasks across 2,500 diverse environments. New files: - src/lerobot/envs/robocasa.py: gym.Env wrapper with deferred env creation, flat 12D action / 16D state vectors, 3-camera support - docs/source/robocasa.mdx: user-facing documentation - docker/Dockerfile.benchmark.robocasa: CI benchmark image Modified files: - src/lerobot/envs/configs.py: RoboCasaEnv config (--env.type=robocasa) - pyproject.toml: robocasa optional dependency group - docs/source/_toctree.yml: sidebar entry - .github/workflows/benchmark_tests.yml: integration test job Refs: https://arxiv.org/abs/2603.04356, https://robocasa.ai Related: huggingface/lerobot#321 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
111 lines
4.0 KiB
Plaintext
111 lines
4.0 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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Pass individual task class names directly as `--env.task` (e.g., `CloseFridge`, `PickPlaceCoffee`). Multiple tasks can be comma-separated for multi-task evaluation.
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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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python -m robocasa.scripts.download_kitchen_assets
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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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## Evaluation
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### Single-task evaluation
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Evaluate a policy on a single RoboCasa task:
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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 \
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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 multiple tasks at once by passing a comma-separated list:
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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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- `--env.task` accepts individual task names (comma-separated).
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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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### Policy inputs and outputs
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**Observations:**
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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.image` -- left agent view (`robot0_agentview_left`), 256x256 HWC uint8
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- `observation.images.image2` -- wrist camera view (`robot0_eye_in_hand`), 256x256 HWC uint8
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- `observation.images.image3` -- right agent view (`robot0_agentview_right`), 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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### 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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## Training
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### Example training command
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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}/robocasa-test \
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--policy.load_vlm_weights=true \
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--dataset.repo_id=your-robocasa-dataset \
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--env.type=robocasa \
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--env.task=CloseFridge \
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--output_dir=./outputs/ \
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--steps=100000 \
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--batch_size=4 \
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--eval.batch_size=1 \
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--eval.n_episodes=1 \
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--eval_freq=1000
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```
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