2 citations · 2 across the 13 of their papers we have counts for
15 papers
Function beyond Form: Functional Correspondence for Cross-Embodiment Dexterous Grasp Generation
Bolin Zou, Wenlong Dong, Mu Ai +4
Cross-embodiment dexterous grasp generation remains challenging because robotic hands differ substantially in geometry, topology, and kinematics. Existing approaches often lack exp…
VISTA: Visually Inferred Spatial ConTact Attention for Contact-Rich Manipulation
Jiayi Chen, Wenlong Dong, Yan Huang +5
Contact-rich manipulation requires precise interaction feedback. While vision-centric imitation learning is prevalent, external visual observations provide indirect and ambiguous c…
Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control
Jun Chen, Erdent Bao, Erdemt Bao +8
Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. However, generalizing to multi-step…
GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping
Zanjia Tong, Wenlong Dong, Chengjie Zhang +1
Task-oriented grasping performance degrades significantly when object views suffer from occlusions. Existing task-oriented grasping methods typically assume task-relevant regions a…
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
Dehao Huang, Aoxiang Gu, Chengjie Zhang +5
Task-success confidence estimation for Vision-Language-Action (VLA) models provides a crucial task-level signal for monitoring manipulation in open-world environments and supportin…
Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations
Chao Tang, Jiacheng Xu, Haofei Lu +4
Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vast diversity of objects and t…
Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert
Chengjie Zhang, Chao Tang, Wenlong Dong +3
Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior work primarily focuses on reducing…
Meta-Memory: Retrieving and Integrating Semantic-Spatial Memories for Robot Spatial Reasoning
Yufan Mao, Hanjing Ye, Wenlong Dong +2
Navigating complex environments requires robots to effectively store observations as memories and leverage them to answer human queries about spatial locations, which is a critical…
MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence
Chao Tang, Anxing Xiao, Yuhong Deng +5
Imitating tool manipulation from human videos offers an intuitive approach to teaching robots, while also providing a promising and scalable alternative to labor-intensive teleoper…
Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop
Tianxing Chen, Kaixuan Wang, Zhaohui Yang +96
Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical…
HGDiffuser: Efficient Task-Oriented Grasp Generation via Human-Guided Grasp Diffusion Models
Dehao Huang, Wenlong Dong, Chao Tang +1
Task-oriented grasping (TOG) is essential for robots to perform manipulation tasks, requiring grasps that are both stable and compliant with task-specific constraints. Humans natur…
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation
Chao Tang, Anxing Xiao, Yuhong Deng +5
Learning tool use from a single human demonstration video offers a highly intuitive and efficient approach to robot teaching. While humans can effortlessly generalize a demonstrate…
Leveraging Semantic and Geometric Information for Zero-Shot Robot-to-Human Handover
Jiangshan Liu, Wenlong Dong, Jiankun Wang +1
Human-robot interaction (HRI) encompasses a wide range of collaborative tasks, with handover being one of the most fundamental. As robots become more integrated into human environm…
RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment
Wenlong Dong, Dehao Huang, Jiangshan Liu +2
Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on co…
FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models
Chao Tang, Dehao Huang, Wenlong Dong +2
Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone t…