titleInternW0-Δ: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
authorsXingyu Miao, Zizun Li, Baole Fang, Kaiwen Song, Tenghui Wang, Hanxue Zhang, Yating Wang, Xudong Li, Yuping He, Xueyuan Wei, Chao Gao, Xijie Yang, Yingxiang Xu, Kerui Ren, Wenqi Guo, Jianjun Zhou, Xinzhe Wang, Weiguang Zhao, Ni Yang, Zetao Cai, Yufei Xue, Hengjie Li, Zeyu He, Yuanzhen Zhou, Rong Fu, Jianyang Zhang, Siwei Cui, Fuxian Huang, Yunsong Zhou, Xing Gao, Yifei Yao, Qiaojun Yu, Kailin Li, Ming Zhou, Mu Huang, Xinyue Li, Wenze Cui, Bingqi Jiang, Xueyue Zhu, Junting Dong, Haoyu Guo, Tao Lu, Mulin Yu, Bowen Zhou, Bin Zhao, Tianfan Xue, Weinan Zhang, Chunhua Shen
date2026-09-25
venuearXiv (preprint, not peer reviewed)
idarXiv:2609.31394
upvotes29 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractWorld Action Models (WAMs) jointly model visual dynamics and action generation for generalist robot manipulation. A central challenge is to integrate priors from large-scale pretrained models---including visual dynamics, scene semantics, geometry, and motion---into a unified framework for robot action generation. We introduce InternW0-Δ, a unified WAM pretrained on a heterogeneous corpus that outperforms prior methods across simulation benchmarks and real-robot platforms. InternW0-Δ combines pretrained visual dynamics, scene-level semantics, 4D geometric and motion priors, and action generation within a Mixture-of-Transformers (MoT) framework. A pretrained video expert and an action expert interact under semantic guidance from a frozen VLM, while a pretrained 4D foundation model injects geometric and motion priors through training-only distillation. We further introduce Causal Imprint, which learns future-relevant scene changes from training-only future supervision and provides predictive representations directly to the action expert without future-video rollout at inference. For large-scale joint training, we construct a heterogeneous corpus of robot demonstrations, UMI data, egocentric human demonstrations, and Ego2Robot data, curated and aligned under a common state-action representation. The resulting corpus contains over 20K hours of processed training data, to our knowledge the largest open-source corpus of its kind. We pretrain InternW0-Δ on this corpus and demonstrate strong performance across simulation benchmarks and real-robot platforms. We will open source the training code, model weights, infrastructure, data-processing pipeline, and processed data where licenses permit. Project page: https://internrobotics.github.io/InternW0-Delta/

cite

@misc{miao2026internw,
  title={InternW0-Δ: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data},
  author={Xingyu Miao and Zizun Li and Baole Fang and Kaiwen Song and Tenghui Wang and Hanxue Zhang and Yating Wang and Xudong Li and Yuping He and Xueyuan Wei and Chao Gao and Xijie Yang and Yingxiang Xu and Kerui Ren and Wenqi Guo and Jianjun Zhou and Xinzhe Wang and Weiguang Zhao and Ni Yang and Zetao Cai and Yufei Xue and Hengjie Li and Zeyu He and Yuanzhen Zhou and Rong Fu and Jianyang Zhang and Siwei Cui and Fuxian Huang and Yunsong Zhou and Xing Gao and Yifei Yao and Qiaojun Yu and Kailin Li and Ming Zhou and Mu Huang and Xinyue Li and Wenze Cui and Bingqi Jiang and Xueyue Zhu and Junting Dong and Haoyu Guo and Tao Lu and Mulin Yu and Bowen Zhou and Bin Zhao and Tianfan Xue and Weinan Zhang and Chunhua Shen},
  year={2026},
  eprint={2609.31394},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2609.31394}
}