9 papers
Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations
Wei-Jin Huang, Yue-Yi Zhang, Yi-Lin Wei +5
Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data.…
ZeroDexGrasp: Zero-Shot Task-Oriented Dexterous Grasp Synthesis with Prompt-Based Multi-Stage Semantic Reasoning
Juntao Jian, Yi-Lin Wei, Chengjie Mou +5
Task-oriented dexterous grasping holds broad application prospects in robotic manipulation and human-object interaction. However, most existing methods still struggle to generalize…
OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback
Yi-Lin Wei, Zhexi Luo, Yuhao Lin +4
Enabling robots to dexterously grasp and manipulate objects based on human commands is a promising direction in robotics. However, existing approaches are challenging to generalize…
TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
Yuhao Lin, Yi-Lin Wei, Haoran Liao +6
Dexterous teleoperation plays a crucial role in robotic manipulation for real-world data collection and remote robot control. Previous dexterous teleoperation mostly relies on hand…
ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation
Ling-An Zeng, Guohong Huang, Yi-Lin Wei +4
We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels. Unli…
iManip: Skill-Incremental Learning for Robotic Manipulation
Zexin Zheng, Jia-Feng Cai, Xiao-Ming Wu +3
The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task,…