5 citations · 16 across the 15 of their papers we have counts for
7 papers · 1 filter
UniFlowWAM: A Unified World-Action Model with Visual Semantics and 4D Geometry
Jialin Tian, Zhuofan Li, Xinkai Wang +5
Robot demonstrations record what the robot saw and what it did. They do not specify how the 3D world changed in between. Scene-level 3D flow describes it by tracking 3D scene point…
InternW0: A Foundational Physical World Model for Efficient Real-World Interactions
Jisong Cai, Yao Mu, Ganlin Yang +23
Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We introduce InternW0, the first…
Skel-WAM: A Hand-Skeleton-Conditioned World Action Model for Human-to-Robot Manipulation Transfer
Zetao Cai, Yaping Li, Yiqun Wang +8
Robot demonstrations are expensive to collect and often provide limited distributional coverage of task variations. Human videos offer a low-cost source of complementary manipulati…
EgoAfford: Task-Oriented Affordance Grounding via Egocentric Referring Segmentation
Xinyuan Guan, Feifan Chen, Xinyu Zhan +3
Part-level affordance grounding has advanced the localization of functional object regions associated with elemental actions. Extending this capability to complex tasks calls for c…
ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning
Bokai Lin, Yifu Xu, Xinyu Zhan +6
Visual signals play a crucial role in policy learning by enabling models to capture object motion and interaction dynamics. Just as humans reason about actions using both past expe…
LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment
Yifu Xu, Bokai Lin, Xinyu Zhan +4
Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction data. However, bridging the embod…
LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior
Xinkai Wang, Chenyi Wang, Yifu Xu +7
We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models reg…