docs(robocerebra): align page with adding_benchmarks template

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.

Made-with: Cursor

docs/source/robocerebra.mdx

@@ -1,14 +1,25 @@
 # 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]"
@@ -18,29 +29,47 @@ pip install -e ".[libero]"
 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.