5 citations · 16 across the 15 of their papers we have counts for
18 papers
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…
OmniCam: Unified Multimodal Video Generation via Camera Control
Xiaoda Yang, Jiayang Xu, Kaixuan Luan +9
Camera control, which achieves diverse visual effects by changing camera position and pose, has attracted widespread attention. However, existing methods face challenges such as co…
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…
AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons
Hongjie Fang, Chenxi Wang, Yiming Wang +11
Scaling up robotic imitation learning for real-world applications requires efficient and scalable demonstration collection methods. While teleoperation is effective, it depends on…
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…
Multi-view Hand Reconstruction with a Point-Embedded Transformer
Lixin Yang, Licheng Zhong, Pengxiang Zhu +4
This work introduces a novel and generalizable multi-view Hand Mesh Reconstruction (HMR) model, named POEM, designed for practical use in real-world hand motion capture scenarios.…
OAKINK2: A Dataset of Bimanual Hands-Object Manipulation in Complex Task Completion
Xinyu Zhan, Lixin Yang, Yifei Zhao +5
We present OAKINK2, a dataset of bimanual object manipulation tasks for complex daily activities. In pursuit of constructing the complex tasks into a structured representation, OAK…
CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation
Kailin Li, Lixin Yang, Haoyu Zhen +6
In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn…
POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo
Lixin Yang, Jian Xu, Licheng Zhong +4
Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks. Previous methods usually encode the 3D information of multi-view ster…
OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction
Lixin Yang, Kailin Li, Xinyu Zhan +4
Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's int…
ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis
Kailin Li, Lixin Yang, Xinyu Zhan +4
Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses…
CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction
Lixin Yang, Xinyu Zhan, Kailin Li +3
Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has bee…