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About

Maolin Lei is a robotics researcher specializing in dual-arm robotic systems, motion planning, and modular reconfigurable manipulators. His work addresses some of the most pressing challenges in robot autonomy and safety, particularly in unstructured and human-shared environments. Lei's most influential contribution is a real-time self-collision avoidance algorithm for dual-arm robots (2020, 20 citations), which introduced a novel discrete spherical boundary collision model to prevent costly hardware damage. Building on this foundation, he developed sophisticated control frameworks integrating time-optimal path parameterization (TOPP) with model predictive control (MPC) for dynamic nonprehensile transportation tasks — work that has garnered 17 citations and demonstrates his ability to bridge theoretical rigor with practical manipulation challenges. His research further extends into adaptive replanning under uncertainty, disturbance estimation for collaborative object transport, and task-driven design optimization of modular reconfigurable robots. Most recently, his involvement in the CONCERT project reflects a growing interest in construction robotics, targeting real-world deployment of reconfigurable cobots on active construction sites. Collectively, Lei's portfolio — spanning over 60 citations — reflects a coherent research vision: enabling safe, intelligent, and adaptable robotic manipulation in complex, dynamic environments. His work is particularly valuable for researchers in human-robot collaboration and industrial automation.