| --- |
| license: mit |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - vision-language-action |
| - vla |
| - diffusion-policy |
| - imitation-learning |
| - ensemble-learning |
| - robotwin |
| library_name: pytorch |
| --- |
| |
| # EnsembleVLA — Released Checkpoints |
|
|
| Released checkpoints for **EnsembleVLA: Ensemble Learning for Vision-Language |
| Action Models**. |
|
|
| - 💻 **Code:** https://github.com/MingC715/EnsembleVLA |
| - 📄 **Paper:** ICML 2026 (coming soon) |
|
|
| EnsembleVLA is an energy-based framework for principled composition of diverse |
| Vision-Language-Action (VLA) policies. It formulates diffusion-based and |
| flow-based VLA models under a unified energy perspective, where additive energy |
| aggregation induces policy composition at the distribution level. Multiple |
| pretrained policies stay **frozen** while a lightweight ensemble head with |
| learnable composition weights and confidence-aware gating aggregates them into a |
| stronger policy, evaluated on the RoboTwin2 rollout interface. |
|
|
| ## What's in this repository |
|
|
| Two released composition families, each over 8 RoboTwin2 tasks. For every task we |
| release the lightweight **ensemble head** plus the two **frozen base policies**: |
|
|
| | Family | Base policy 1 | Base policy 2 | |
| | --- | --- | --- | |
| | `dp+dp3` | Diffusion Policy (DP) | 3D Diffusion Policy (DP3) | |
| | `dp+pi0.5` | Diffusion Policy (DP) | pi0.5 / openpi | |
|
|
| **Tasks:** `beat_block_hammer`, `click_alarmclock`, `dump_bin_bigbin`, |
| `handover_block`, `move_playingcard_away`, `open_laptop`, `place_bread_skillet`, |
| `stack_bowls_three`. |
|
|
| ## Repository layout |
|
|
| Files live at the repository root and mirror the code's `best_checkpoint/` layout: |
|
|
| ```text |
| dp+dp3/<task>/ |
| ensemble_checkpoint/best.pt # lightweight EnsembleVLA head |
| base_dp/<ckpt>.ckpt # frozen DP base policy |
| base_dp3/<ckpt>.ckpt # frozen DP3 base policy |
| dp+pi0.5/<task>/ |
| ensemble_checkpoint/best.pt # lightweight EnsembleVLA head |
| base_dp/<ckpt>.ckpt # frozen DP base policy |
| base_pi05_checkpoint_dir/ |
| model.safetensors # frozen pi0.5 base policy weights |
| metadata.pt |
| assets/<task>/norm_stats.json |
| ``` |
|
|
| > The pi0.5 base needs all three of `model.safetensors`, `metadata.pt`, and |
| > `assets/` from the same `base_pi05_checkpoint_dir/`. |
| |
| ## Download |
| |
| Download everything straight into the code repo's `best_checkpoint/` directory: |
|
|
| ```bash |
| pip install -U huggingface_hub |
| huggingface-cli download mingchens/EnsembleVLA --repo-type model --local-dir best_checkpoint |
| ``` |
|
|
| Then follow the **Environment Setup** and **Evaluation** instructions in the |
| [GitHub README](https://github.com/MingC715/EnsembleVLA). Only inference |
| checkpoints are required for evaluation; optimizer states and training/rollout |
| logs are not included. The full checkpoint manifest (per-task base checkpoints |
| and results) is in |
| [`docs/checkpoints.md`](https://github.com/MingC715/EnsembleVLA/blob/main/docs/checkpoints.md). |
|
|
| ## License |
|
|
| Released under the [MIT License](https://github.com/MingC715/EnsembleVLA/blob/main/LICENSE). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{song2026ensemblevla, |
| title={EnsembleVLA: Ensemble Learning for Vision-Language Action Models}, |
| author={Song, Mingchen and Deng, Xiang and Wei, Jie and Jiang, Dongmei and Nie, Liqiang and Guan, Weili}, |
| booktitle={International Conference on Machine Learning}, |
| year={2026} |
| } |
| ``` |
|
|
