no code implementations • 21 Feb 2024 • Justin Lidard, Haimin Hu, Asher Hancock, Zixu Zhang, Albert Gimó Contreras, Vikash Modi, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Leonard, María Santos, Jaime Fernández Fisac
As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question.
no code implementations • 28 May 2023 • Justin Lidard, Oswin So, Yanxia Zhang, Jonathan DeCastro, Xiongyi Cui, Xin Huang, Yen-Ling Kuo, John Leonard, Avinash Balachandran, Naomi Leonard, Guy Rosman
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
1 code implementation • 23 Sep 2022 • Justice Mason, Christine Allen-Blanchette, Nicholas Zolman, Elizabeth Davison, Naomi Leonard
In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not.
no code implementations • 28 Jan 2022 • Udaya Ghai, Udari Madhushani, Naomi Leonard, Elad Hazan
We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances.
no code implementations • 8 Oct 2021 • Udari Madhushani, Naomi Leonard
We propose \textit{ComEx}, a novel cost-effective communication protocol in which the group achieves the same order of performance as full communication while communicating only $O(\log T)$ number of messages.
no code implementations • NeurIPS Workshop ICBINB 2020 • Udari Madhushani, Naomi Leonard
We identify a fundamental drawback of natural extensions of Upper Confidence Bound (UCB) algorithms to the multi-agent bandit problem in which multiple agents facing the same explore-exploit problem can share information.
no code implementations • 2 Sep 2020 • Udari Madhushani, Naomi Leonard
To do so we study a class of distributed stochastic bandit problems in which agents communicate over a multi-star network and make sequential choices among options in the same uncertain environment.