3 papers
cs.RO2026
Learning Task-Invariant Properties via Dreamer: Enabling Efficient Policy Transfer for Quadruped Robots
Junyang Liang, Yuxuan Liu, Yabin Chang +5
Achieving quadruped robot locomotion across diverse and dynamic terrains presents significant challenges, primarily due to the discrepancies between simulation environments and rea…
cs.RO2025
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning
Changxin Huang, Junyang Liang, Yanbin Chang +2
Enabling a high-degree-of-freedom robot to learn specific skills is a challenging task due to the complexity of robotic dynamics. Reinforcement learning (RL) has emerged as a promi…
cs.RO2024
Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution
Changxin Huang, Yanbin Chang, Junfan Lin +3
The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied intelligence. Although reinforcement learning meth…