Naomi Ehrich Leonard — accepted AI-conference papers

2025

Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions

Haimin Hu, Jaime Fernández Fisac, Naomi Ehrich Leonard, Deepak E. Gopinath, Jonathan A. DeCastro, Guy Rosman

ICRA 2025

Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overtaking “corridor” opens. While an expert human can do well at making such time-sensitive decisions, autonomous agents are

Cited by 9 Source ↗Scholar ↗

2023

Proactive Opinion-Driven Robot Navigation Around Human Movers

Charlotte Cathcart, María Santos, Shinkyu Park, Naomi Ehrich Leonard

IROS 2023poster

We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's “opinion” for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's ob…

Cited by 25 Source ↗Scholar ↗

2022

Decentralized Learning With Limited Communications for Multi-robot Coverage of Unknown Spatial Fields

Kensuke Nakamura, María Santos, Naomi Ehrich Leonard

IROS 2022poster

This paper presents an algorithm for a team of mobile robots to simultaneously learn a spatial field over a domain and spatially distribute themselves to optimally cover it. Drawing from previous approaches that estimate the spatial field through a centralized Gaussian process, this work leverages t…

Cited by 10 Source ↗Scholar ↗

2021

Multi-Robot Task Allocation Games in Dynamically Changing Environments

Shinkyu Park, Yaofeng Desmond Zhong, Naomi Ehrich Leonard

ICRA 2021poster

We propose a game-theoretic multi-robot task allocation framework that enables a large team of robots to optimally allocate tasks in dynamically changing environments. As our main contribution, we design a decision-making algorithm that defines how the robots select tasks to perform and how they rep…

Cited by 41 Source ↗Scholar ↗