Chong Zhang

Papers

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About

Chong Zhang is a robotics researcher whose work spans legged robot locomotion, multi-robot coordination, and autonomous navigation. His research addresses some of the most technically demanding challenges in mobile robotics, combining control theory, reinforcement learning, and deep learning to push the boundaries of what autonomous systems can achieve in complex, real-world environments. Zhang's most recognized contribution lies in quadrupedal robot control, where his 2024 work on fall prediction, control, and recovery has already garnered 11 citations, reflecting its immediate relevance to the field. This research tackles a critical vulnerability in legged systems — instability and recovery — making quadruped robots more resilient for practical deployment. Complementing this, his attention-based map encoding framework for legged locomotion introduces a generalizable approach to navigating diverse terrains with sparse footholds, demonstrating sophisticated integration of perception and motion planning. Beyond legged systems, Zhang has contributed to multi-robot coverage planning, proposing a hierarchical reinforcement learning strategy with dense segmented Siamese networks for large-scale environment coverage — directly applicable to disaster response and environmental monitoring missions. Together, these works position Zhang as a versatile contributor bridging fundamental robotics control with scalable autonomous systems, with growing influence evidenced by his accumulating citation record across multiple high-impact research directions.