lerobot/docs/source/libero.mdx

# LIBERO

**LIBERO** is a benchmark designed to study **lifelong robot learning**. The idea is that robots won’t just be pretrained once in a factory, they’ll need to keep learning and adapting with their human users over time. This ongoing adaptation is called **lifelong learning in decision making (LLDM)**, and it’s a key step toward building robots that become truly personalized helpers.

- 📄 [LIBERO paper](https://arxiv.org/abs/2306.03310)
- 💻 [Original LIBERO repo](https://github.com/Lifelong-Robot-Learning/LIBERO)

To make progress on this challenge, LIBERO provides a set of standardized tasks that focus on **knowledge transfer**: how well a robot can apply what it has already learned to new situations. By evaluating on LIBERO, different algorithms can be compared fairly and researchers can build on each other’s work.

LIBERO includes **five task suites**:

- **LIBERO-Spatial (`libero_spatial`)** – tasks that require reasoning about spatial relations.
- **LIBERO-Object (`libero_object`)** – tasks centered on manipulating different objects.
- **LIBERO-Goal (`libero_goal`)** – goal-conditioned tasks where the robot must adapt to changing targets.
- **LIBERO-90 (`libero_90`)** – 90 short-horizon tasks from the LIBERO-100 collection.
- **LIBERO-Long (`libero_10`)** – 10 long-horizon tasks from the LIBERO-100 collection.

Together, these suites cover **130 tasks**, ranging from simple object manipulations to complex multi-step scenarios. LIBERO is meant to grow over time, and to serve as a shared benchmark where the community can test and improve lifelong learning algorithms.

![An overview of the LIBERO benchmark](https://libero-project.github.io/assets/img/libero/fig1.png)

## Evaluating with LIBERO

At **LeRobot**, we ported [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) into our framework and used it mainly to **evaluate [SmolVLA](https://huggingface.co/docs/lerobot/en/smolvla)**, our lightweight Vision-Language-Action model.

LIBERO is now part of our **multi-eval supported simulation**, meaning you can benchmark your policies either on a **single suite of tasks** or across **multiple suites at once** with just a flag.

To Install LIBERO, after following LeRobot official instructions, just do:
`pip install -e ".[libero]"`

### Single-suite evaluation

Evaluate a policy on one LIBERO suite:

```bash
python src/lerobot/scripts/eval.py \
  --policy.path="your-policy-id" \
  --env.type=libero \
  --env.task=libero_object \
  --eval.batch_size=2 \
  --eval.n_episodes=3
```

- `--env.task` picks the suite (`libero_object`, `libero_spatial`, etc.).
- `--eval.batch_size` controls how many environments run in parallel.
- `--eval.n_episodes` sets how many episodes to run in total.

---

### Multi-suite evaluation

Benchmark a policy across multiple suites at once:

```bash
python src/lerobot/scripts/eval.py \
  --policy.path="your-policy-id" \
  --env.type=libero \
  --env.task=libero_object,libero_spatial \
  --eval.batch_size=1 \
  --eval.n_episodes=2
```

- Pass a comma-separated list to `--env.task` for multi-suite evaluation.

### Policy inputs and outputs

When using LIBERO through LeRobot, policies interact with the environment via **observations** and **actions**:

- **Observations**
  - `observation.state` – proprioceptive features (agent state).
  - `observation.images.image` – main camera view (`agentview_image`).
  - `observation.images.image2` – wrist camera view (`robot0_eye_in_hand_image`).

  ⚠️ **Note:** LeRobot enforces the `.images.*` prefix for any multi-modal visual features. Always ensure that your policy config `input_features` use the same naming keys, and that your dataset metadata keys follow this convention during evaluation.
  If your data contains different keys, you must rename the observations to match what the policy expects, since naming keys are encoded inside the normalization statistics layer.
  This will be fixed with the upcoming Pipeline PR.

- **Actions**
  - Continuous control values in a `Box(-1, 1, shape=(7,))` space.

We also provide a notebook for quick testing:
Training with LIBERO

## Training with LIBERO

When training on LIBERO tasks, make sure your dataset parquet and metadata keys follow the LeRobot convention.

The environment expects:

- `observation.state` → 8-dim agent state
- `observation.images.image` → main camera (`agentview_image`)
- `observation.images.image2` → wrist camera (`robot0_eye_in_hand_image`)

⚠️ Cleaning the dataset upfront is **cleaner and more efficient** than remapping keys inside the code.
To avoid potential mismatches and key errors, we provide a **preprocessed LIBERO dataset** that is fully compatible with the current LeRobot codebase and requires no additional manipulation:
👉 [HuggingFaceVLA/libero](https://huggingface.co/datasets/HuggingFaceVLA/libero)

For reference, here is the **original dataset** published by Physical Intelligence:
👉 [physical-intelligence/libero](https://huggingface.co/datasets/physical-intelligence/libero)

---

### Example training command

```bash
python src/lerobot/scripts/train.py \
  --policy.type=smolvla \
  --policy.repo_id=${HF_USER}/libero-test \
  --dataset.repo_id=jadechoghari/smol-libero3 \
  --env.type=libero \
  --env.task=libero_10 \
  --output_dir=./outputs/ \
  --steps=100000 \
  --batch_size=4 \
  --eval.batch_size=1 \
  --eval.n_episodes=1 \
  --eval_freq=1000 \
```

---

### Note on rendering

LeRobot uses MuJoCo for simulation. You need to set the rendering backend before training or evaluation:

- `export MUJOCO_GL=egl` → for headless servers (e.g. HPC, cloud)