Chong Di

Papers

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About

Chong Di is a robotics researcher focused on advancing autonomous systems in challenging, unstructured environments. His primary research areas include wheeled mobile robot (WMR) locomotion on deformable terrain, robotic arm trajectory planning, and human-robot interaction for medical applications. Di’s most significant contribution is his pioneering work on slippage estimation for planetary rovers operating on loose soil. In his highly cited 2024 paper, he introduced a novel few-shot learning framework that uses wheel-rut images to predict slippage, directly addressing a critical safety and control issue for robots on extraterrestrial surfaces. This work, which has already garnered 10 citations, offers a practical solution for real-time motion control without the need for extensive training data. Di has also developed a self-optimizing deep reinforcement learning method for robotic arm trajectory planning in dynamic environments, and a compliance control system for rehabilitation massage robots that ensures safe human-robot interaction despite unpredictable patient movements. By tackling core problems in field robotics and medical robotics, Chong Di is establishing himself as a researcher dedicated to making robots more adaptive, safe, and reliable in the real world.