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About

Chuanyu Yang is a robotics researcher whose work spans robot locomotion, manipulation, and sensorimotor learning, with a particular emphasis on applying deep reinforcement learning to develop adaptive, intelligent robotic systems. His most significant contributions center on enabling robots to acquire complex motor skills that generalize to unpredictable real-world conditions. His multi-expert learning architecture (MELA), cited 50 times, demonstrated how robots can adaptively blend representative expert skills to achieve versatile locomotion — a meaningful advance in robot adaptability. Complementing this, his hierarchical deep reinforcement learning framework for push recovery and balancing behaviors, garnering 26 citations, has informed how legged robots maintain stability across diverse physical challenges. Yang has also made notable strides in robotic grasping, addressing difficult scenarios such as occluded or ungraspable object configurations through bimanual manipulation strategies. His 2023 work on fall recovery for legged robots and identifying critical sensory feedback for motor learning further demonstrates his commitment to practical robot deployment in unstructured environments. With research also touching on tactile sensing and mobile manipulation, Yang's body of work collectively advances the frontier of autonomous, physically capable robots across both locomotion and dexterous manipulation domains.