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About

Guanda Li is a leading researcher at the intersection of soft robotics, bio-inspired locomotion, and deep reinforcement learning. His work focuses on solving the fundamental challenge of controlling highly compliant, redundant robotic systems—from underwater soft robots to bipedal and quadrupedal walkers. Li’s major contribution is the development of data-driven control frameworks that integrate central pattern generators (CPGs) with reinforced reflex neural networks, enabling adaptive, energy-efficient locomotion in complex environments. His 2021 paper on a deep reinforcement learning framework for underwater soft robot locomotion has garnered 41 citations, establishing a foundational approach for data-based control of soft-bodied systems. Li has also demonstrated that soft-body dynamics and joint elasticity significantly enhance energy efficiency in undulatory swimming, verified through deep learning studies. His recent work on AI-CPG for bipedal locomotion (2024, 24 citations) and two-stage learning for quadrupedal locomotion on uneven terrain (2025) showcases his ongoing impact in bridging bio-inspired principles with modern machine learning. With a growing citation record and a focus on practical, adaptive control, Li is shaping the future of autonomous, soft-bodied robots.