Papers
43
Total Citations
1,043
H-Index
17
About
Garrett Warnell is a robotics researcher whose work sits at the intersection of autonomous navigation, machine learning, and human-robot interaction. His research primarily focuses on enabling mobile robots to navigate intelligently and adaptively in complex, human-populated environments — a challenge that spans classical planning, imitation learning, and reinforcement learning. Warnell's most influential contribution is a comprehensive survey on machine learning for mobile robot navigation (2022, 234 citations), which has become a key reference for researchers entering the field. He is perhaps best known for developing the APPL family of algorithms — APPLD, APPLI, APPLR, and APPLE — a suite of adaptive planner parameter learning methods that allow navigation systems to automatically tune themselves through demonstrations, interventions, reinforcement, and evaluative feedback, reducing reliance on expert re-tuning. His Socially Compliant Navigation Dataset (SCAND), with over 100 citations since 2022, has become a valuable community resource for training socially aware robots. Beyond ground robots, Warnell has explored gaze-driven MAV control and visual-only imitation learning, demonstrating a broad commitment to accessible, practical human-robot collaboration. His cumulative citation record reflects a growing and meaningful impact on how autonomous systems learn to move safely and intelligently alongside people.
Research Focus
Key Achievements
Top Papers
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- 3APPLD: Adaptive Planner Parameter Learning From Demonstration66 citations · 2020
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- 5APPL: Adaptive Planner Parameter Learning43 citations · 2022
- 6VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation42 citations · 2022
- 7Human Gaze-Driven Spatial Tasking of an Autonomous MAV42 citations · 2019
- 8APPLI: Adaptive Planner Parameter Learning From Interventions40 citations · 2021
- 9APPLR: Adaptive Planner Parameter Learning from Reinforcement39 citations · 2021
- 10APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback33 citations · 2021