6 papers · 1 filter
SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space
Ruiteng Zhao, Zhengshen Zhang, Yue Su +6
World Action Models (WAMs) couple action generation with prediction of future states. Their effectiveness depends on whether future dynamics are modeled in a space that is both ali…
CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos
Chubin Zhang, Jianan Wang, Zifeng Gao +5
Generalist Vision-Language-Action models remain constrained by the scarcity of robotic data relative to the abundance of human video demonstrations. Existing Latent Action Models a…
World Guidance: World Modeling in Condition Space for Action Generation
Yue Su, Sijin Chen, Haixin Shi +7
Leveraging future observation modeling to facilitate action generation presents a promising avenue for enhancing the capabilities of Vision-Language-Action (VLA) models. However, e…
DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
Yue Su, Chubin Zhang, Sijin Chen +4
Learning whole-body mobile manipulation via imitation is essential for generalizing robotic skills to diverse environments and complex tasks. However, this goal is hindered by sign…
Motion Before Action: Diffusing Object Motion as Manipulation Condition
Yue Su, Xinyu Zhan, Hongjie Fang +3
Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning…
Dense Policy: Bidirectional Autoregressive Learning of Actions
Yue Su, Xinyu Zhan, Hongjie Fang +5
Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, ha…