z-lab / flashvla-pi05-robotwin

FlashVLA · π0.5 · RoboTwin 2.0

Streaming Action Decoding for Fast and Asynchronous VLA Inference

A π0.5 flow-matching vision-language-action policy finetuned on RoboTwin 2.0 (50-task multitask) and served with FlashVLA streaming action decoding for fast, asynchronous inference.

• Base model: lerobot/pi05_base
• Method: FlashVLA , streaming action decoding for flow-matching VLAs (async chunk-overlap execution)
• Benchmark: RoboTwin 2.0, 50-task multitask (clean / randomized)

Results

RoboTwin 2.0 50-task multitask success rate (%). d is the async step delay: d=0 is synchronous, d=1 and d=2 overlap the next chunk's inference with execution.

Usage

Install FlashVLA:

git clone https://github.com/z-lab/flashvla.git
cd flashvla
conda env create -f environment.yml
conda activate flashvla

RoboTwin 2.0 evaluation uses a server/client split across two environments. After the one-time setup in sim_eval/robotwin/ , from $ROBOTWIN/policy/pi05_flashvla/ :

bash eval_server.sh                      # terminal 1, flashvla env, starts the policy server
ROBOTWIN_VENV=... bash eval_client.sh    # terminal 2, RoboTwin env, runs the SAPIEN sim

Training configs for this checkpoint are in train/configs/pi05/robotwin/ .

License

These weights are finetuned from lerobot/pi05_base , which is released under the Gemma Terms of Use . Those terms govern model derivatives, so they apply to this checkpoint and to anything derived from it, including the Gemma Prohibited Use Policy . If you redistribute this checkpoint or a derivative of it, you must pass the same terms along.

The FlashVLA inference and training code is separately released under the Apache 2.0 License .

Citation

@inproceedings{li2026flashvla,
  title     = {{FlashVLA: Streaming Action Decoding for Fast and Asynchronous VLA Inference}},
  author    = {Li, Zekai and Tang, Jiaming and Liu, Zhijian},
  booktitle = {Conference on Robot Learning (CoRL)},
  year      = {2026}
}