About

Jia Pan is a distinguished robotics researcher whose work spans motion planning, collision detection, multi-robot navigation, tactile sensing, and human-robot collaboration. His contributions have fundamentally advanced how robots perceive, navigate, and interact with complex environments. Pan's early landmark contributions include the widely adopted FCL (Flexible Collision Library), a high-performance collision detection framework that has become a standard tool in the robotics community (537 citations), and the Sequential Convex Optimization approach to motion planning, which elegantly solves trajectory generation problems among obstacles (840 citations). His ITOMP algorithm further extended these ideas to dynamic, real-time environments. Pan has also pioneered deep reinforcement learning methods for decentralized multi-robot collision avoidance, producing a series of influential works that enable robot teams to navigate safely without centralized coordination — research that has collectively attracted over a thousand citations. His 2021 work on soft magnetic tactile skin, achieving super-resolution force sensing (522 citations), demonstrates his versatility beyond planning into robotic perception. More recently, Pan has turned his attention to deformable object manipulation and construction robotics, pushing robots toward more nuanced real-world applications. With numerous highly cited papers across diverse subfields, his research has profoundly shaped modern robotics.

Research Focus

Key Achievements

33
H-Index
133
Papers
6,170
Total Citations
46
Avg Citations/Paper
🏆 Most Cited Paper
Motion planning with sequential convex optimization and convex collision checking
840 citations · 2014
📈 Most Prolific Year: 2021 (16 Papers)
🤝 Key Collaborators: 278
🏛 Institutions: University of California, Berkeley, University of North Carolina at Chapel Hill, City University of Hong Kong, University of Hong Kong, Hong Kong University of Science and Technology, National Institute of Advanced Industrial Science and Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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