Papers
71
Total Citations
2,443
H-Index
24
About
Claire J. Tomlin is a pioneering researcher at the intersection of control theory, robotics, and machine learning, with particular expertise in safety-critical autonomous systems. Her work has fundamentally advanced how robots and autonomous vehicles can operate reliably in complex, uncertain environments. Tomlin's most influential contributions center on reachability-based safety frameworks, which provide rigorous mathematical guarantees that autonomous systems—particularly aerial robots like quadrotors—remain within safe operational bounds even while learning. Her 2014 paper on safe learning with Gaussian processes (262 citations) exemplifies this approach, bridging reinforcement learning with formal safety verification. She has also made significant strides in learning-based model predictive control, pursuit-evasion game theory for multi-robot coordination, and data-driven safety filters using Hamilton-Jacobi methods and control barrier functions. More recently, her work on confidence-aware motion prediction (115 citations) and visually guided navigation addresses the challenge of real-world human-robot interaction. Across her portfolio, Tomlin consistently tackles the tension between the flexibility of machine learning and the hard guarantees demanded by safety-critical applications, making her research indispensable to the fields of autonomous systems, robotics, and hybrid control theory.
Research Focus
Key Achievements
Top Papers
- 1Reachability-based safe learning with Gaussian processes262 citations · 2014
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- 3Guaranteed decentralized pursuit-evasion in the plane with multiple pursuers146 citations · 2011
- 4Applications of hybrid reachability analysis to robotic aerial vehicles126 citations · 2011
- 5On efficient sensor scheduling for linear dynamical systems122 citations · 2012
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