7 papers · 1 filter
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…
DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
Hanxiang Ren, Pei Zhou, Xunzhe Zhou +1
Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entanglement provides a pathway for ob…
DexHoldem: Playing Texas Hold'em with Dexterous Embodied System
Feng Chen, Tianzhe Chu, Li Sun +6
Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene, choose a context-appropriate…
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-…
HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data
Ruizhe Liu, Pei Zhou, Qian Luo +4
Effective generalization in robotic manipulation requires representations that capture invariant patterns of interaction across environments and tasks. We present a self-supervised…
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…