3 papers
cs.RO2025
Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion
Lingfan Bao, Tianhu Peng, Chengxu Zhou
This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the…
cs.RO2025
Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng +2
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on…
cs.RO2025
Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion
Tianhu Peng, Lingfan Bao, Chengxu Zhou
We present a unified gait-conditioned reinforcement learning framework that enables humanoid robots to perform standing, walking, running, and smooth transitions within a single re…