Daoxiong Gong
Papers
26
Total Citations
382
H-Index
9
About
Daoxiong Gong is a robotics researcher whose work spans autonomous navigation, legged locomotion, human-robot interaction, and intelligent manipulation. His early contributions established strong foundations in robot pathfinding and motion optimization: his comparative study of A-star algorithms for search-and-rescue applications (2011, 102 citations) remains a widely referenced benchmark in maze-solving research, while his comprehensive review of evolutionary computation methods for legged robot gait optimization (2010, 70 citations) helped consolidate a generation of research in bio-inspired locomotion. His development of a pneumatic tactile sensor for cooperative robots (2017, 56 citations) demonstrated a practical commitment to enabling safe and effective human-robot collaboration. More recently, Gong has advanced the frontier of deep reinforcement learning applied to robotic manipulation, tackling challenging problems such as grasping fully occluded objects, obstacle avoidance for serial manipulators, and push-grasp synergy in cluttered environments. His work on adversarial imitation learning further addresses the real-world challenge of training robots from imperfect demonstrations. Complementing these efforts, his research on intuitive teleoperation and motion mapping between heterogeneous master-slave systems underscores a sustained focus on making complex robotic systems accessible and practical for real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1A comparative study of A-star algorithms for search and rescue in perfect maze102 citations · 2011
- 2A Review of Gait Optimization Based on Evolutionary Computation70 citations · 2010
- 3A Pneumatic Tactile Sensor for Co-Operative Robots56 citations · 2017
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