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  1. ASSET_SHA256SUMS +9 -0
  2. LICENSE +202 -0
  3. NOTICE +11 -0
  4. README.md +72 -0
  5. action_vae.pt +3 -0
  6. policy.pt +3 -0
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  8. stats/libero_goal.json +1938 -0
  9. stats/libero_object.json +1938 -0
  10. stats/libero_spatial.json +1938 -0
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LICENSE ADDED
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NOTICE ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ FastWAM LIBERO checkpoints
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+ Copyright (c) 2026 Advanced Micro Devices, Inc.
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+
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+ These checkpoints contain a modified version of Wan2.2-TI2V-5B
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+ (https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B, Wan Team, Alibaba), licensed
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+ under the Apache License, Version 2.0. The Wan2.2-TI2V-5B weights were
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+ fine-tuned by Advanced Micro Devices, Inc. using the FastWAM training recipe
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+ (https://github.com/yuantianyuan01/FastWAM, MIT License).
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+
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+ Training data: LIBERO (Liu et al., NeurIPS 2023; CC BY 4.0), in the LeRobot
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+ format published as yuanty/LIBERO-fastwam (CC BY 4.0).
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: Wan-AI/Wan2.2-TI2V-5B
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+ datasets:
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+ - yuanty/LIBERO-fastwam
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+ library_name: pytorch
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+ tags:
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+ - robotics
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+ - world-action-model
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+ - libero
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+ ---
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+
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+ # FastWAM LIBERO
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+
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+ A latent world-action policy for the [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) benchmark, fine-tuned by Advanced Micro Devices, Inc. from [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B).
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+
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+ Each 32-step action chunk is encoded as 8 latent tokens of width 16. On the four LIBERO suites the policy reaches **97.2%** average success with a single sample.
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+
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+ ## Files
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+
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+ | File | Description |
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+ |---|---|
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+ | `policy.pt` | Inference weights for the video and action experts |
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+ | `action_vae.pt` | Suite-conditioned action VAE |
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+ | `stats/libero_spatial.json` | Normalization statistics for LIBERO-Spatial |
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+ | `stats/libero_object.json` | Normalization statistics for LIBERO-Object |
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+ | `stats/libero_goal.json` | Normalization statistics for LIBERO-Goal |
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+ | `stats/libero_10.json` | Normalization statistics for LIBERO-10 |
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+
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+ Use the statistics file that matches the suite under evaluation. `policy.pt` contains inference weights only.
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+
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+ SHA-256 checksums are in [`ASSET_SHA256SUMS`](ASSET_SHA256SUMS).
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+
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+ ## Runtime weights
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+
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+ This repository does not include the Wan2.2 VAE, the umT5-XXL text encoder, or the umT5-XXL tokenizer. Download them from their upstream repositories:
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+
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+ ```bash
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+ huggingface-cli download Wan-AI/Wan2.2-TI2V-5B \
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+ Wan2.2_VAE.pth models_t5_umt5-xxl-enc-bf16.pth \
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+ --local-dir checkpoints/Wan-AI/Wan2.2-TI2V-5B
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+ huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B \
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+ --include "google/umt5-xxl/*" \
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+ --local-dir checkpoints/Wan-AI/Wan2.1-T2V-1.3B
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+ ```
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+
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+ The full Wan video DiT is not required. `policy.pt` already contains the fine-tuned video and action experts.
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+
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+ LIBERO simulator assets, BDDL task files, and initial states are also required.
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+
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+ ## Intended use
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+
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+ Research on world-action models and robot-policy inference, in simulation.
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+
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+ ## Limitations
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+
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+ - Evaluated only in the LIBERO simulator. Not validated on physical robots.
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+ - Not intended for safety-critical or high-stakes applications. Users are responsible for evaluating the policy and adding appropriate safety mechanisms.
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+ - Use must also follow the Wan2.2 model card.
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+
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+ ## License
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+
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+ These weights are licensed under the Apache License 2.0 ([`LICENSE`](LICENSE)). See [`NOTICE`](NOTICE).
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+
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+ They are a modified version of [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) (Apache-2.0, Wan Team, Alibaba), fine-tuned by Advanced Micro Devices, Inc. The training recipe is [FastWAM](https://github.com/yuantianyuan01/FastWAM) (MIT). That license covers the training code, not these weights.
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+
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+ Training data: [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) (CC BY 4.0), in the LeRobot format published as [yuanty/LIBERO-fastwam](https://huggingface.co/datasets/yuanty/LIBERO-fastwam) (CC BY 4.0).
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+
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+ ## Citation
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+
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+ - T. Yuan, Z. Dong, Y. Liu, and H. Zhao, "Fast-WAM: Do World Action Models Need Test-time Future Imagination?" arXiv:2603.16666, 2026.
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+ - B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone, "LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning," NeurIPS 2023.
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