---
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.