WAM-Adapter

WAM-Adapter: Efficiently Adapting Video-DiT World Representations to Robot Actions

Dongxu Liu^1,2,  Zhe Li^1,  Zundong Ke¹  Zhipeng Zhang^1,†

¹AutoLab, School of Artificial Intelligence, Shanghai Jiao Tong University
²Awaken Robotics
(*Equal contribution, †Corresponding author)

📖 Overview

WAM-Adapter is a parameter- and training-efficient framework that adapts a frozen Video-DiT world model (Wan2.1-T2V-1.3B) into a robot World Action Model:

• Frozen Video-DiT + LoRA. The 1.3B backbone stays frozen; rank-64 LoRA adapts it to robot data.
• One joint current+future pass. The clean current frame and noisy future latent slots are processed in a single Video-DiT forward — no future-video denoising loop, no extra action-side forward.
• Temporal-group cross-layer readout. Multi-layer representations are organized into Current / Early-Future / Late-Future groups with group-specific cross-layer fusion, and the whole action chunk is predicted by a small mixed-context action decoder.

Only 121M trainable parameters (with a compact 1.3B backbone, no embodied pretraining): 89.0% RoboTwin 2.0 (50 tasks), 98.5% LIBERO, 75.17% LIBERO-Plus — while improving training throughput by 9.66× over Fast-WAM under the same setting.

📦 Checkpoints

File	Benchmark	Training steps	Size
wam_adapter_robotwin50_240k.pt	RoboTwin 2.0 (50 tasks)	240k	3.08 GB
wam_adapter_libero_spatial_70k.pt	LIBERO-Spatial	70k	3.08 GB
wam_adapter_libero_object_60k.pt	LIBERO-Object	60k	3.08 GB
wam_adapter_libero_goal_80k.pt	LIBERO-Goal	80k	3.08 GB
wam_adapter_libero_long_80k.pt	LIBERO-Long	80k	3.08 GB

Each checkpoint is a PyTorch file containing the LoRA-adapted backbone (mot), the temporal-group readout (action_decoder) and its configuration.

🚀 Usage

from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="ldx825/WAM-Adapter",
    filename="wam_adapter_robotwin50_240k.pt",
)
# then, inside the WAM-Adapter codebase:
# model = ...  # build WAMAdapter (see the GitHub repository)
# model.load_checkpoint(ckpt_path)

Please refer to the GitHub repository for the complete training / evaluation / deployment instructions.

🔗 Links

• GitHub: https://github.com/ldx825/WAM-Adapter
• Project page: https://ldx825.github.io/WAM-Adapter-Homepage/
• Backbone: Wan-AI/Wan2.1-T2V-1.3B

📝 Citation

The arXiv release is coming soon; for now please cite the project as:

@article{wamadapter2026,
  title   = {WAM-Adapter: Efficiently Adapting Video-DiT World Representations to Robot Actions},
  author  = {Liu, Dongxu and Li, Zhe and Ke, Zundong and Zhang, Zhipeng},
  journal = {arXiv preprint},
  year    = {2026},
}