policy_fastwam_README

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This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

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Research Paper

Paper: https://arxiv.org/abs/2603.16666

Repository

Code: https://github.com/yuantianyuan01/FastWAM

Project page: https://yuantianyuan01.github.io/FastWAM/

Citation

@article{yuan2026fastwam,
  title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
  author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
  journal = {arXiv preprint arXiv:2603.16666},
  year = {2026},
  url = {https://arxiv.org/abs/2603.16666}
}

Additional Resources

Base video model: https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B

Released upstream checkpoints: https://huggingface.co/yuanty/fastwam

Results

Evaluated on LIBERO with ZibinDong/fastwam_libero_uncond_2cam224:

Suite	Success rate	n_episodes
libero_spatial	97.6%	500
libero_object	99.0%	500
libero_goal	95.0%	500
libero_10	94.0%	500
average	96.4%	2000

Reproduce: lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300.

For LIBERO-10, use --env.task=libero_10 --env.episode_length=600:

lerobot-eval \
    --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
    --policy.device=cuda \
    --policy.torch_dtype=float32 \
    --policy.n_action_steps=10 \
    --env.type=libero \
    --env.task=libero_10 --env.observation_height=256 --env.observation_width=256 \
    --eval.batch_size=1 \
    --eval.n_episodes=50 \
    --seed=0 --env.episode_length=600