About

Jonathan P. How is a prominent robotics and autonomous systems researcher whose work spans autonomous navigation, motion planning, and multi-robot coordination. A professor at MIT, How has made foundational contributions to some of the most pressing challenges in modern robotics, consistently bridging theoretical rigor with real-world applicability. How's research on socially aware motion planning using deep reinforcement learning (715 citations) transformed how robots navigate pedestrian-rich environments, enabling machines to internalize nuanced human behavioral norms. His path-following guidance methods for unmanned aerial vehicles (423 citations) established critical stability frameworks still referenced in UAV research today. His team's involvement in the 2007 DARPA Urban Challenge produced influential work on autonomous urban driving and RRT-based motion planning (350 and 215 citations respectively), shaping a generation of self-driving vehicle research. More recently, How has advanced the frontiers of high-speed UAV navigation through the FASTER trajectory planner (157 citations) and aggressive collision avoidance systems (129 citations), while contributing Kimera-Multi (237 citations), a landmark distributed SLAM system for multi-robot teams. His search-and-rescue UAV work further demonstrates his commitment to humanitarian applications. Across more than 2,600 citations from these papers alone, How's career reflects a remarkable synthesis of theoretical innovation and transformative practical impact.

Research Focus

Key Achievements

37
H-Index
123
Papers
5,620
Total Citations
46
Avg Citations/Paper
🏆 Most Cited Paper
Socially aware motion planning with deep reinforcement learning
715 citations · 2017
📈 Most Prolific Year: 2017 (14 Papers)
🤝 Key Collaborators: 218
🏛 Institutions: Massachusetts Institute of Technology, Decision Systems (United States), Stanford University, IIT@MIT, American Institute of Aeronautics and Astronautics, Northeastern University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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