Instructions to use ServoVLA/ServoVLA-LIBERO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use ServoVLA/ServoVLA-LIBERO with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| library_name: servovla | |
| license: mit | |
| tags: | |
| - robotics | |
| - vision-language-action | |
| - lerobot | |
| - libero | |
| pipeline_tag: robotics | |
| # ServoVLA LIBERO | |
| ServoVLA policy for LIBERO-40 simulation. This release contains the EMA policy | |
| head from training step 1,190,000 and the action normalization statistics saved | |
| with that run. | |
| ## Model Contract | |
| - Benchmark: LIBERO-40 (`libero_10`, `libero_spatial`, `libero_object`, `libero_goal`) | |
| - Action space: 7-dimensional direct environment action | |
| - State space: 8-dimensional LIBERO proprioception | |
| - Cameras: `observation.images.image`, `observation.images.image2` | |
| - Action chunk: 16 steps | |
| - Inference steps: 3 | |
| - Vision encoder: `facebook/dinov3-vitb16-pretrain-lvd1689m` | |
| - Language encoder: `Qwen/Qwen3.5-0.8B` | |
| - Weights: EMA (`used_ema=true`) | |
| - Action normalization: enabled, per-horizon dimension, 16 x 7 statistics | |
| ## Evaluation | |
| The selected checkpoint was evaluated on the fixed LIBERO-40 four-suite | |
| protocol with 30 episodes per task, 1,200 episodes total: | |
| - Overall balanced success rate: 78.0% | |
| - `libero_10`: 79.0% | |
| - `libero_spatial`: 75.0% | |
| - `libero_object`: 84.33% | |
| - `libero_goal`: 73.67% | |
| Three additional 50-episode-per-task evaluations with different seeds produced | |
| overall balanced success rates of 75.6%, 79.15%, and 69.25% (mean 75.95%). | |
| Rollouts used relative LIBERO environment control, open-loop 16-step chunks, | |
| and observed maximum frame delay 0 in the reported runs. | |
| ## Files | |
| - `model.safetensors`: LeRobot-compatible EMA policy weights. | |
| - `config.json`: ServoVLA policy configuration. | |
| - `policy_preprocessor.json` and `policy_postprocessor.json`: LeRobot processor configuration. | |
| - `checkpoint.pt`: cleaned ServoVLA checkpoint containing `model_state`, `ema_state`, | |
| `step`, and `action_normalization` only. | |
| The checkpoint is weights-only for inference and fresh initialization. Optimizer, | |
| scheduler, W&B, and resume-training state are intentionally absent. | |
| ## Usage | |
| Use this directory with the ServoVLA LIBERO evaluation entry points. The LIBERO | |
| camera keys, state ordering, action dimensions, and direct-action semantics must | |
| match the model contract above. This policy is not compatible with SO101 robot | |
| observations or the SO101 delta-action contract. | |
| ## License | |
| ServoVLA is released under the MIT License. The vision and language encoders and | |
| the LIBERO benchmark assets are subject to their respective upstream licenses | |
| and terms. | |