5 papers
Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
Yunchao Zhang, Yijia Weng, Ruizhe Liu +3
Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-d…
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…
SoftSkill: Behavioral Compression for Contextual Adaptation
Xijia Tao, Yihua Teng, Xinyu Fu +6
Agent skills are commonly deployed as natural-language Markdown files that encode answer policies, evidence-use habits, and task procedures. These files are readable and portable,…
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…
HyperTASR: Hypernetwork-Driven Task-Aware Scene Representations for Robust Manipulation
Li Sun, Jiefeng Wu, Feng Chen +2
Effective policy learning for robotic manipulation requires scene representations that selectively capture task-relevant environmental features. Current approaches typically employ…