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docs(robocerebra): align page with adding_benchmarks template

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Rework docs/source/robocerebra.mdx to follow the standard benchmark
doc structure: intro + links + available tasks + installation + eval
+ recommended episodes + policy I/O + training + reproducing results.

- Point everything at lerobot/smolvla_robocerebra (the released
  checkpoint), not the personal pepijn223 mirror.
- Add the --env.fps=20 and --env.obs_type=pixels_agent_pos flags
  that CI actually uses, so copy-paste eval reproduces CI.
- Split the "Training" block out of the recipe section into its own
  section with the feature table.
- Add an explicit "Reproducing published results" section pointing
  at the CI smoke eval.

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# RoboCerebra
[RoboCerebra](https://robocerebra-project.github.io/) is a long-horizon manipulation benchmark for evaluating high-level reasoning, planning, and memory in VLAs. Episodes chain multiple sub-goals with language-grounded intermediate instructions, building on top of LIBERO's simulator stack.
[RoboCerebra](https://robocerebra-project.github.io/) is a long-horizon manipulation benchmark that evaluates **high-level reasoning, planning, and memory** in VLAs. Episodes chain multiple sub-goals with language-grounded intermediate instructions, built on top of LIBERO's simulator stack (MuJoCo + robosuite, Franka Panda 7-DOF).
- Paper: [RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation](https://arxiv.org/abs/2506.06677)
- Project website: [robocerebra-project.github.io](https://robocerebra-project.github.io/)
- Dataset: [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) — LeRobot v3.0, 6,660 episodes (571,116 frames) at 20 fps, 1,728 language-grounded sub-tasks
- Dataset: [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) — LeRobot v3.0, 6,660 episodes / 571,116 frames at 20 fps, 1,728 language-grounded sub-tasks.
- Pretrained policy: [`lerobot/smolvla_robocerebra`](https://huggingface.co/lerobot/smolvla_robocerebra)
## Available tasks
RoboCerebra reuses LIBERO's simulator, so evaluation runs against the LIBERO `libero_10` long-horizon suite:
| Suite | CLI name | Tasks | Description |
| --------- | ----------- | ----- | ------------------------------------------------------------- |
| LIBERO-10 | `libero_10` | 10 | Long-horizon kitchen/living room tasks chaining 3–6 sub-goals |
Each RoboCerebra episode in the dataset is segmented into multiple sub-tasks with natural-language instructions, which the unified dataset exposes as independent supervision signals.
## Installation
RoboCerebra reuses LIBERO's simulator stack, so install the `libero` extra:
RoboCerebra piggybacks on LIBERO, so the `libero` extra is all you need:
```bash
pip install -e ".[libero]"
```
<Tip>
RoboCerebra requires Linux (MuJoCo/robosuite). Set the rendering backend before training or evaluation:
RoboCerebra requires Linux (MuJoCo / robosuite). Set the rendering backend before training or evaluation:
```bash
export MUJOCO_GL=egl # for headless servers
export MUJOCO_GL=egl # for headless servers (HPC, cloud)
```
</Tip>
## Evaluation
RoboCerebra eval runs against the LIBERO `libero_10` long-horizon suite with RoboCerebra's camera naming (`image` + `wrist_image`) and an extra empty-camera slot for policies expecting three views:
RoboCerebra eval runs against LIBERO's `libero_10` suite with RoboCerebra's camera naming (`image` + `wrist_image`) and an extra empty-camera slot for policies trained with three views:
```bash
lerobot-eval \
--policy.path=pepijn223/smolvla_robocerebra \
--policy.path=lerobot/smolvla_robocerebra \
--env.type=libero \
--env.task=libero_10 \
--env.fps=20 \
--env.obs_type=pixels_agent_pos \
--eval.batch_size=1 \
--eval.n_episodes=10 \
'--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.wrist_image": "observation.images.camera2"}' \
--policy.empty_cameras=3
```
### Recommended evaluation episodes
**10 episodes per task** across the `libero_10` suite (100 total) for reproducible benchmarking. Matches the protocol used in the RoboCerebra paper.
## Policy inputs and outputs
**Observations:**
- `observation.state` — 8-dim proprioceptive state (7 joint positions + gripper)
- `observation.images.image` — third-person view, 256×256 HWC uint8
- `observation.images.wrist_image` — wrist-mounted camera view, 256×256 HWC uint8
**Actions:**
- Continuous control in `Box(-1, 1, shape=(7,))` — end-effector delta (6D) + gripper (1D)
## Training
The unified dataset at [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) exposes two RGB streams:
The unified dataset at [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) exposes two RGB streams and language-grounded sub-task annotations:
| Feature | Shape | Description |
| -------------------------------- | ------------- | -------------------- |
@@ -59,3 +88,7 @@ lerobot-train \
--env.task=libero_10 \
--output_dir=outputs/smolvla_robocerebra
```
## Reproducing published results
The released checkpoint [`lerobot/smolvla_robocerebra`](https://huggingface.co/lerobot/smolvla_robocerebra) was trained on `lerobot/robocerebra_unified` and evaluated with the command in the [Evaluation](#evaluation) section. CI runs the same command with `--eval.n_episodes=1` as a smoke test on every PR touching the benchmark.