Papers
10
Total Citations
597
H-Index
7
About
Jeremy Gillula is a robotics and control systems researcher whose work sits at the critical intersection of machine learning, safety theory, and autonomous systems. He is best known for pioneering frameworks that enable robots and autonomous vehicles to learn and adapt in real time while providing rigorous mathematical guarantees against unsafe behavior — a challenge that has long limited the deployment of learning-based control in safety-critical environments. His most influential contribution, "Reachability-based safe learning with Gaussian processes" (2014, 262 citations), introduced a principled method for combining reachability analysis with reinforcement learning, ensuring constraint satisfaction even during the learning process itself. This line of work extended through several notable papers, including guaranteed safe online learning for quadrotor target tracking (92 citations) and a general safety framework for uncertain robotic systems (33 citations), collectively establishing him as a foundational voice in safe reinforcement learning for robotics. Gillula's early career reflects impressive breadth: as an undergraduate, he contributed to Caltech's entry in the 2005 DARPA Grand Challenge, and his doctoral research on hybrid systems for robotic aerial vehicles (126 citations) demonstrated how complex nonlinear systems could be modeled and controlled with formal safety guarantees. His body of work has amassed over 590 citations, making him an important reference point for researchers building trustworthy autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Reachability-based safe learning with Gaussian processes262 citations · 2014
- 2Applications of hybrid reachability analysis to robotic aerial vehicles126 citations · 2011
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- 4Hybrid Systems in Robotics38 citations · 2011
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- 8Guaranteed safe online learning of a bounded system4 citations · 2011
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- 10A Probabilistic Framework for Real-Time Mapping on an Unmanned Ground Vehicle2 citations · 2006