feat(envs): add RoboCasa365 benchmark integration

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>

docs/source/_toctree.yml

@@ -79,6 +79,8 @@
     title: LIBERO
   - local: metaworld
     title: Meta-World
+  - local: robocasa
+    title: RoboCasa365
   - local: envhub_isaaclab_arena
     title: NVIDIA IsaacLab Arena Environments
   title: "Benchmarks"

docs/source/robocasa.mdx

@@ -0,0 +1,110 @@
+# 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
+```