About

Jian Mi’s research lies at the intersection of robotics, control systems, and intelligent sensing, with a primary focus on enabling mobile robots to navigate and operate autonomously in complex, real-world environments. His most significant contributions center on low-cost, high-accuracy self-localization for indoor mobile robots using HF-band RFID systems. By pioneering configurations with multiple readers and passive tag textiles, Mi demonstrated that reliable Monte-Carlo localization could be achieved without expensive sensors—a breakthrough that has garnered over 60 citations across his core RFID papers. He further extended his work into humanoid robotics, developing novel methods for whole-body joint angle estimation using Gaussian process dynamical models and particle filters, enabling real-time imitation learning. More recently, Mi has tackled the critical challenge of safe path planning in human-shared spaces, proposing reinforcement learning frameworks and multi-policy rapidly-exploring random tree controllers that account for stochastic human motion. His 2025 papers on safe planners reflect an ongoing commitment to bridging theoretical control with practical deployment. With a career spanning from foundational RFID systems to cutting-edge human-robot interaction, Jian Mi’s work continues to shape how robots perceive, plan, and act alongside people.

Research Focus

Key Achievements

6
H-Index
11
Papers
91
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Design of an HF-Band RFID System with Multiple Readers and Passive Tags for Indoor Mobile Robot Self-Localization
26 citations · 2016
📈 Most Prolific Year: 2025 (3 Papers)
🤝 Key Collaborators: 24
🏛 Institutions: University of Fukui, Osaka University of Economics, Peking University, Yangzhou Vocational University, Yangzhou University, Xidian University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago