add docs for eval

docs/source/libero.mdx

@@ -0,0 +1,79 @@
+# 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.
+
+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.
+
+![Libero Figure](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?utm_source=chatgpt.com) into our framework and used it mainly to **evaluate 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 \
+  --env.multitask_eval=False \
+  --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 \
+  --env.multitask_eval=True \
+  --eval.batch_size=1 \
+  --eval.n_episodes=2
+```
+
+- Pass a comma-separated list to `-env.task` for multi-suite evaluation.
+- Set `-env.multitask_eval=True` to enable evaluation across all tasks in those suites.
+
+### 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 visual features. Make sure your dataset metadata keys match this convention when evaluating.
+    
+- **Actions**
+    - Continuous control values in a `Box(-1, 1, shape=(7,))` space.
+
+We also provide a notebook for quick testing:

src/lerobot/envs/libero.py

@@ -148,9 +148,9 @@ def get_libero_dummy_action():
     return [0, 0, 0, 0, 0, 0, -1]
 
 
-ACTION_DIM = 8
-
 
+OBS_STATE_DIM = 8
+ACTION_DIM = 7
 class LiberoEnv(gym.Env):
     metadata = {"render_modes": ["rgb_array"], "render_fps": 80}
 
@@ -234,13 +234,13 @@ class LiberoEnv(gym.Env):
                     "agent_pos": spaces.Box(
                         low=-1000.0,
                         high=1000.0,
-                        shape=(ACTION_DIM,),
+                        shape=(OBS_STATE_DIM,),
                         dtype=np.float64,
                     ),
                 }
             )
 
-        self.action_space = spaces.Box(low=-1, high=1, shape=(7,), dtype=np.float32)
+        self.action_space = spaces.Box(low=-1, high=1, shape=(ACTION_DIM,), dtype=np.float32)
 
     def render(self):
         raw_obs = self._env.env._get_observations()