Author

Kai Wang

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Preprint Oct 2026

ViDAL: A Visual Dynamics-Grounded Action Latent Space for Vision-Language-Action Models

Vision-Language-Action (VLA) models have become a central paradigm for robot policy learning, which predict actions in three forms: raw action chunks, discrete action tokens, or continuous action latents. However, existing action representations primarily model action trajectories, with limited consideration of the vis...

Yuan Xu, Yi-Xiang Chen, Qi-Sen Ma et al. · 0 citations

Jul 2026

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control, and introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.

GigaWorld Team, Angen Ye, Ang-Yuan Ma et al. · arXiv.org · 3 citations

Preprint Aug 2026

XEWorld: Can Action-Conditioned World Models Generalize to Unseen Robot Embodiments?

XEWorld is introduced, a controlled cross-embodiment testbed for world models that isolates embodiments by evaluating held-out robots within physically identical scenes, highlighting that achieving true cross-embodiment generalization requires architectural innovations that decouple visual appearance from underlying ph...

Yixiang Chen, Jiabing Yang, Yuan Xu et al. · 0 citations