RoboCerebra

RoboCerebra 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
Project website: robocerebra-project.github.io
Dataset: 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

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 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:

bash

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

</Tip>

Evaluation

RoboCerebra eval runs against LIBERO's libero_10 suite with RoboCerebra's camera naming (image + wrist_image) and an extra empty-camera slot so a three-view-trained policy receives the expected input layout:

bash

lerobot-eval \
  --policy.path=lerobot/smolvla_robocerebra \
  --env.type=libero \
  --env.task=libero_10 \
  --env.fps=20 \
  --env.obs_type=pixels_agent_pos \
  --env.observation_height=256 \
  --env.observation_width=256 \
  '--env.camera_name_mapping={"agentview_image": "image", "robot0_eye_in_hand_image": "wrist_image"}' \
  --eval.batch_size=1 \
  --eval.n_episodes=10 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.wrist_image": "observation.images.camera2"}' \
  --policy.empty_cameras=1

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 exposes two RGB streams and language-grounded sub-task annotations:

Feature	Shape	Description
`observation.images.image`	(256, 256, 3)	Third-person view
`observation.images.wrist_image`	(256, 256, 3)	Wrist-mounted camera
`observation.state`	(8,)	Joint pos + gripper
`action`	(7,)	EEF delta + gripper

Fine-tune a SmolVLA base on it:

bash

lerobot-train \
  --policy.path=lerobot/smolvla_base \
  --dataset.repo_id=lerobot/robocerebra_unified \
  --env.type=libero \
  --env.task=libero_10 \
  --output_dir=outputs/smolvla_robocerebra

Reproducing published results

The released checkpoint lerobot/smolvla_robocerebra was trained on lerobot/robocerebra_unified and evaluated with the command in the Evaluation section. CI runs the same command with --eval.n_episodes=1 as a smoke test on every PR touching the benchmark.