5 papers
PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning
Huanyu Li, Dewei Wang, Xinmiao Wang +4
Humanoid robots often need to balance competing objectives, such as maximizing speed while minimizing energy consumption. While current reinforcement learning (RL) methods can mast…
InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills
Dayang Liang, Yuhang Lin, Xinzhe Liu +3
Interaction is one of the core abilities of humanoid robots. However, most existing frameworks focus on non-interactive whole-body control, which limits their practical applicabili…
Learning Soccer Skills for Humanoid Robots: A Progressive Perception-Action Framework
Jipeng Kong, Xinzhe Liu, Yuhang Lin +4
Soccer presents a significant challenge for humanoid robots, demanding tightly integrated perception-action capabilities for tasks like perception-guided kicking and whole-body bal…
MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains
Dewei Wang, Xinmiao Wang, Xinzhe Liu +4
Humanoid robots have demonstrated robust locomotion capabilities using Reinforcement Learning (RL)-based approaches. Further, to obtain human-like behaviors, existing methods integ…
Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Jiyuan Shi, Xinzhe Liu, Dewei Wang +6
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches…