Wil Thomason
Papers
10
Total Citations
178
H-Index
6
About
Wil Thomason is a roboticist whose research sits at the intersection of socially-aware navigation, integrated task and motion planning (TMP), and high-speed motion planning. His most impactful work, "Social Momentum," with over 100 combined citations, introduces a framework for socially competent robot navigation in crowded, unstructured environments—addressing the critical challenge of how humans perceive and react to robot motion. This work has been foundational in enabling mobile robots to integrate seamlessly into pedestrian scenes. Thomason has also made significant contributions to integrated TMP, notably through "Task and Motion Informed Trees (TMIT*)," which provides almost-surely asymptotically optimal solutions for hybrid discrete-continuous planning problems. His recent work on "Motions in Microseconds" achieves a remarkable 500x speedup over state-of-the-art sampling-based planners, bringing planning times for high-degree-of-freedom robots down to microseconds. Additionally, his research on zero-shot gesture recognition and stochastic implicit neural functions for safe planning under sensing uncertainty demonstrates a commitment to robust, human-aware autonomy. Thomason’s work is characterized by its practical impact, pushing the boundaries of what robots can achieve in real-world, human-centered environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Social Momentum47 citations · 2018
- 3Motions in Microseconds via Vectorized Sampling-Based Planning22 citations · 2024
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- 7Object Reconfiguration with Simulation-Derived Feasible Actions2 citations · 2023
- 8Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions2 citations · 2023
- 9Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions2 citations · 2021
- 10AORRTC: Almost-Surely Asymptotically Optimal Planning With RRT-Connect1 citations · 2025