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About

Peijin Li is a leading researcher in soft robotics, specializing in the design, control, and real-world application of compliant manipulators. Their work bridges the gap between theoretical control algorithms and practical, unstructured interactions—such as opening doors and pulling drawers—tasks that remain notoriously difficult for rigid robots. Li’s most influential paper, “Hierarchical control of soft manipulators towards unstructured interactions” (2021), has garnered 132 citations, demonstrating its significant impact on the field. This work introduced a hierarchical framework that enables soft arms to perform daily interaction tasks with robustness and adaptability. Li has also advanced reinforcement learning for soft robotics, developing methods like Q-learning with rough simulators to overcome sample inefficiency (10 citations) and motion control under pose constraints for Honeycomb Pneumatic Network (HPN) arms (11 citations). Their foundational paper on the design, control, and applications of a multi-segment soft robotic arm (2020) established key principles for achieving high load capacity while preventing buckling. Through these contributions, Li is shaping a future where soft robots safely and effectively assist humans in everyday environments.