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Date: Fri, 5 Sep 2025 09:55:42 +0000
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---
 docs/source/libero.mdx | 49 +++++++++++++++++++++++-------------------
 1 file changed, 27 insertions(+), 22 deletions(-)

diff --git a/docs/source/libero.mdx b/docs/source/libero.mdx
index 013d01836..8c4ba532e 100644
--- a/docs/source/libero.mdx
+++ b/docs/source/libero.mdx
@@ -15,7 +15,7 @@ LIBERO includes **five task suites**:
 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)
-*Figure 1: An overview of the LIBERO benchmark.*
+_Figure 1: An overview of the LIBERO benchmark._
 
 ## Evaluating with LIBERO
 
@@ -23,15 +23,16 @@ At **LeRobot**, we ported [LIBERO](https://github.com/Lifelong-Robot-Learning/LI
 
 LIBERO is now part of our **multi-eval supported simulation**, allowing you to benchmark your policies either on a **single suite of tasks** or across **multiple suites at once** with just a single flag.
 
-To install LIBERO, first follow the [LeRobot Installation Guide](https://huggingface.co/docs/lerobot/installation).  
+To install LIBERO, first follow the [LeRobot Installation Guide](https://huggingface.co/docs/lerobot/installation).
 Once LeRobot is installed, there are two options:
 
-1. **Install via pip** (recommended):  
+1. **Install via pip** (recommended):
+
    ```bash
    pip install "lerobot[libero,smolvla]"
    ```
 
-2. **Install from source**:  
+2. **Install from source**:
    ```bash
    git clone https://github.com/huggingface/lerobot.git
    cd lerobot
@@ -85,9 +86,10 @@ When using LIBERO through LeRobot, policies interact with the environment via **
   - `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.
+
   ## Input Features and Metadata Alignment
 
-  To train or evaluate a policy, you use `make_policy`, which builds a feature-naming dictionary for the observations the policy expects.  
+  To train or evaluate a policy, you use `make_policy`, which builds a feature-naming dictionary for the observations the policy expects.
   This mapping can come from:
   - Dataset metadata
   - The evaluation environment
@@ -96,32 +98,33 @@ When using LIBERO through LeRobot, policies interact with the environment via **
   ### Common Issues
 
   A common problem is when the keys in the dataset, environment, and policy config do not match. For example:
-  - `wrist_image` vs `observation.images.image2`  
-  - `observation.image2` (as in SmolVLA) vs the `.images.*` prefix convention  
+  - `wrist_image` vs `observation.images.image2`
+  - `observation.image2` (as in SmolVLA) vs the `.images.*` prefix convention
 
   Such mismatches will cause `KeyError`s. This may be due to assumptions in `make_policy` or missing error handling.
 
-  ---
+  ***
 
   ### How to Check Expected Features
-
-  - Open your policy config (`config.json`), e.g. [example here](https://huggingface.co/jadechoghari/smolvla-libero/blob/main/config.json).  
+  - Open your policy config (`config.json`), e.g. [example here](https://huggingface.co/jadechoghari/smolvla-libero/blob/main/config.json).
   - Or add a breakpoint in `train.py` and inspect:
-    ```python
-    print(policy.config.input_features)
-  To ensure you can just check what your policy expects as `input_features`:
 
-    - Open your policy config (`config.json`), e.g. [example here](https://huggingface.co/jadechoghari/smolvla-libero/blob/main/config.json).  
+    ````python
+    print(policy.config.input_features)
+    To ensure you can just check what your policy expects as `input_features`:
+
+    - Open your policy config (`config.json`), e.g. [example here](https://huggingface.co/jadechoghari/smolvla-libero/blob/main/config.json).
     - Or add a breakpoint in `train.py` and inspect:
       ```python
       print(policy.config.input_features)
-  Fixing KeyErrors (Preprocessing Example)
+    Fixing KeyErrors (Preprocessing Example)
+    ````
 
 ## Fixing KeyErrors (Preprocessing Example)
 
 If your dataset columns do not follow the expected naming, you can rename them in-place before training:
 
-```python
+````python
 import pyarrow.parquet as pq
 import shutil
 
@@ -162,12 +165,12 @@ The environment expects:
 - `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. We plan to provide a script to easily preprocess such data.
-To avoid potential mismatches and `KeyError`s, we provide a **preprocessed LIBERO dataset** that is fully compatible with the current LeRobot codebase and requires no additional manipulations.  
+To avoid potential mismatches and `KeyError`s, we provide a **preprocessed LIBERO dataset** that is fully compatible with the current LeRobot codebase and requires no additional manipulations.
 
-- 🔗 [Preprocessed LIBERO dataset (Hugging Face LeRobot org)](https://huggingface.co/datasets/HuggingFaceVLA/libero)  
-- 🔗 [Original LIBERO dataset (physical-intelligence)](https://huggingface.co/datasets/physical-intelligence/libero)  
+- 🔗 [Preprocessed LIBERO dataset (Hugging Face LeRobot org)](https://huggingface.co/datasets/HuggingFaceVLA/libero)
+- 🔗 [Original LIBERO dataset (physical-intelligence)](https://huggingface.co/datasets/physical-intelligence/libero)
 
-The preprocessed dataset follows LeRobot naming conventions (e.g., `.images.*` prefix for visual features) and aligns with policy configs out-of-the-box.  
+The preprocessed dataset follows LeRobot naming conventions (e.g., `.images.*` prefix for visual features) and aligns with policy configs out-of-the-box.
 The original dataset is acknowledged here as the primary source.
 ---
 
@@ -187,7 +190,7 @@ python src/lerobot/scripts/train.py \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
   --eval_freq=1000 \
-```
+````
 
 ---
 
@@ -209,15 +212,17 @@ UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to
 
 This happens because Colab’s rendering contexts are **not thread-safe**, and `ThreadPoolExecutor(max_workers=num_workers)` can trigger segfaults or leaked semaphore warnings.
 
-**Colab Note:**  
+**Colab Note:**
 Parallel evaluation is not supported in Colab. To avoid these issues, run sequentially or disable multitask evaluation:
 
 Run sequentially:
+
 ```bash
 --env.max_parallel_tasks=1
 ```
 
 Or disable multitask evaluation:
+
 ```bash
 --env.multitask_eval=False
 ```