18 citations · 31 across the 15 of their papers we have counts for
15 papers
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…
Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking
Tao Huang, Ruofei Liu, Xuchen Tang +15
Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance…
ReactiveBFM: Reactive Closed-Loop Motion Planning Towards Universal Humanoid Whole-Body Control
Xiao Chen, Weishuai Zeng, Xiaojie Niu +12
While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions. As a result, they are vulnerable to en…
Scaling Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Kangning Yin, Xiaojie Niu +15
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making i…
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In pract…
Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching
Weiguang Zhao, Junting Dong, Rui Zhang +3
Teleoperation is a key paradigm for transferring human dexterity to robots, yet most prior work targets objects that are initially static, such as grasping or manipulation. Dynamic…
ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning
Kailin Li, Puhao Li, Tengyu Liu +2
Human hands play a central role in interacting, motivating increasing research in dexterous robotic manipulation. Data-driven embodied AI algorithms demand precise, large-scale, hu…
SemGrasp: Semantic Grasp Generation via Language Aligned Discretization
Kailin Li, Jingbo Wang, Lixin Yang +2
Generating natural human grasps necessitates consideration of not just object geometry but also semantic information. Solely depending on object shape for grasp generation confines…
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…
Color-NeuS: Reconstructing Neural Implicit Surfaces with Color
Licheng Zhong, Lixin Yang, Kailin Li +3
The reconstruction of object surfaces from multi-view images or monocular video is a fundamental issue in computer vision. However, much of the recent research concentrates on reco…
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Daiheng Gao, Yuliang Xiu, Kailin Li +6
Hand, the bearer of human productivity and intelligence, is receiving much attention due to the recent fever of digital twins. Among different hand morphable models, MANO has been…
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…