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About

Kaiqiang Tang is a leading researcher in legged robotics, specializing in motion planning and autonomous navigation for hexapod robots in complex, unstructured environments. His core contributions lie at the intersection of deep reinforcement learning (DRL) and hierarchical control, where he has developed novel frameworks that enable multi-contact locomotion over challenging terrains. Tang’s most influential work, “Hierarchical Free Gait Motion Planning for Hexapod Robots Using Deep Reinforcement Learning” (2023), has garnered 25 citations, demonstrating its impact on advancing robot autonomy. His earlier research, including “Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots” (2021, 13 citations) and “Obstacle avoidance of hexapod robots using fuzzy Q-learning” (2017, 12 citations), established foundational methods for combining reinforcement learning with fuzzy control to achieve safe, adaptive obstacle avoidance. Tang’s innovative approaches, such as incremental reinforcement learning and rule-based shallow-trial techniques, have significantly improved convergence speed and efficiency in robot path planning. His work is essential reading for students and researchers seeking to understand how intelligent algorithms can empower legged robots to traverse real-world obstacles with unprecedented agility and reliability.