6 papers
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 Assistant Experts
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…
Dexterous Manipulation through Imitation Learning: A Survey
Shan An, Ziyu Meng, Chao Tang +9
Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordi…
FlowPlan: Zero-Shot Task Planning with LLM Flow Engineering for Robotic Instruction Following
Zijun Lin, Chao Tang, Hanjing Ye +1
Robotic instruction following tasks require seamless integration of visual perception, task planning, target localization, and motion execution. However, existing task planning met…
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…