1 citations · 2 across the 7 of their papers we have counts for
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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…
ICAT: Incident-Case-Grounded Adaptive Testing for Physical-Risk Prediction in Embodied World Models
Zhenglin Lai, Sirui Huang, Yuteng Li +3
Reference-image-conditioned video generators are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to anticipate physical hazards…
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…
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…
Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning
Runhao Zeng, Dingjie Zhou, Qiwei Liang +6
Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large languag…