1 code implementation • 16 Mar 2024 • Zixuan Wu, Sean Ye, Manisha Natarajan, Matthew C. Gombolay
Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation.
1 code implementation • 20 Jun 2023 • Zixuan Wu, Sean Ye, Manisha Natarajan, Letian Chen, Rohan Paleja, Matthew C. Gombolay
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent.
1 code implementation • 19 Jun 2023 • Sean Ye, Manisha Natarajan, Zixuan Wu, Rohan Paleja, Letian Chen, Matthew C. Gombolay
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction.
no code implementations • 20 Jan 2023 • Mariah L. Schrum, Emily Sumner, Matthew C. Gombolay, Andrew Best
We find that our approach generates driving styles consistent with end-user styles (p<. 001) and participants rate our approach as more similar to their level of aggressiveness (p=. 002).
no code implementations • 7 Jul 2020 • Mariah L. Schrum, Mark Connolly, Eric Cole, Mihir Ghetiya, Robert Gross, Matthew C. Gombolay
Learning to control a safety-critical system with latent dynamics (e. g. for deep brain stimulation) requires taking calculated risks to gain information as efficiently as possible.