4 papers
Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning
Qian Luo, Ruizhe Liu, Pei Zhou +2
We study how trajectory-shape diversity in demonstrations affects imitation learning (IL) performance across models, tasks, and data scales. We introduce Geometric Entropy (H_G), a…
Hyper-GoalNet: Goal-Conditioned Manipulation Policy Learning with HyperNetworks
Pei Zhou, Wanting Yao, Qian Luo +2
Goal-conditioned policy learning for robotic manipulation presents significant challenges in maintaining performance across diverse objectives and environments. We introduce Hyper-…
Learning Human-Humanoid Coordination for Collaborative Object Carrying
Yushi Du, Yixuan Li, Baoxiong Jia +5
Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been ex…
MaxMI: A Maximal Mutual Information Criterion for Manipulation Concept Discovery
Pei Zhou, Yanchao Yang
We aim to discover manipulation concepts embedded in the unannotated demonstrations, which are recognized as key physical states. The discovered concepts can facilitate training ma…