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About

Jianping He is a robotics and autonomous systems researcher whose work spans multi-robot coordination, security vulnerabilities in robotic systems, and safety-critical motion control. He has made significant contributions to understanding how adversaries can exploit robotic networks, most notably through his highly cited work on intelligent physical attacks against mobile robots (24 citations), which demonstrated how an external observer could learn obstacle-avoidance mechanisms to trap robots at predetermined locations. Complementing this security focus, He has pioneered topology inference techniques for multi-robot formation control, revealing how network communication structures can be deduced purely from external observation — a finding with profound implications for both attack and defense strategies. His research extends to safety-critical robotics, including an online control barrier function construction method for manipulator motion control (17 citations) and deep reinforcement learning-based mapless path planning for multi-robot systems. He has also advanced multi-robot patrolling strategies that balance efficiency with unpredictability using multi-agent reinforcement learning. Practically minded, He developed the Robopheus virtual-physical testbed and conducted rigorous benchmarking of physics engines widely used in robotic simulation. Collectively, his publications reflect a researcher shaping the intersection of robotic security, cooperative autonomy, and intelligent control.