Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics. Conversely, world-action models inherit spatiotemporal priors from video generation models, yet remain limited in semant...
Wen-Xuan Song, Jia-Yi Chen, Jing-Bo Wang et al.· 1 citation
World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. However, their multi-view interfaces typically tile images or concatenate tokens, leaving the geometric relationships among synchronized cameras implicit. This makes it...
Wen-Bo Chen, Tian-Fu Li, Hao-Xuan Xu et al.· 0 citations
Vision-Language-Action (VLA) models have become a powerful paradigm for robot manipulation, but training a single generalist policy for heterogeneous robot embodiments remains an open problem. Existing methods have two main limitations. First, they underuse dynamics priors shared across diverse visual and interaction d...
Jun-Feng Li, Junjie He, Zhi-De Zhong et al.· 1 citation
DreamTrajectory is presented, a trajectory-guided framework for language-conditioned mobile manipulation that introduces one component for each limitation of existing Vision-Language-Action policies, and jointly predicts an intention-level end-effector trajectory and a whole-body action chunk in a single action expert.
Zheng Yang, Wen-Jie Zhang, Xiang-Yu Chen et al.· 1 citation
PSG-JEPA is proposed, a physically grounded JEPA world model that shapes its latent space with two complementary grounding objectives beyond forward prediction: grounding individual latents in robot proprioceptive state, and grounding latent pairs in multi-horizon joint-angle changes.
