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About

Yinghao Gan is a pioneering researcher in soft robotics, specializing in the design, control, and application of compliant manipulators for unstructured environments. His work centers on enabling soft robotic arms to perform complex, daily interaction tasks—such as opening doors and pulling drawers—that challenge traditional rigid robots. Gan’s major contributions include the development of the Honeycomb Pneumatic Network (HPN) arm, a novel structure that balances compliance with load capacity, and the introduction of hierarchical control frameworks that allow soft manipulators to navigate unpredictable interactions safely. His most cited paper, “Hierarchical control of soft manipulators towards unstructured interactions” (2021, 132 citations), demonstrates how bio-inspired control strategies can achieve dexterous, real-world tasks. Gan has also advanced reinforcement learning methods for soft robots, proposing a Q-learning approach that leverages rough simulators to overcome sample inefficiency (2021, 10 citations) and a constrained motion control technique for the HPN arm (2022, 11 citations). His research has significant implications for human-robot collaboration, assistive technologies, and industrial automation, making him a key figure in the evolution of safe, adaptive robotic systems.