activity
20192022
most citedMaximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions

6 citations · 6 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CY2022

Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing

Klara Nahrstedt, Naresh Shanbhag, Vikram Adve +25

In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary Research Unit at the University of Illinois Urbana-Champaign, hosted the Future of Computing Symposium to cele…

cs.GT2022

Stackelberg Routing of Autonomous Cars in Mixed-Autonomy Traffic Networks

Maxwell Kolarich, Negar Mehr

As autonomous cars are becoming tangible technologies, road networks will soon be shared by human-driven and autonomous cars. However, humans normally act selfishly which may resul…

math.OC20216 cited

Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions

Negar Mehr, Mingyu Wang, Mac Schwager

In this paper, we study the problem of multiple stochastic agents interacting in a dynamic game scenario with continuous state and action spaces. We define a new notion of stochast…

cs.MA2021

Decentralized Role Assignment in Multi-Agent Teams via Empirical Game-Theoretic Analysis

Fengjun Yang, Negar Mehr, Mac Schwager

We propose a method, based on empirical game theory, for a robot operating as part of a team to choose its role within the team without explicitly communicating with team members,…

cs.RO2021

Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories

Talha Kavuncu, Ayberk Yaraneri, Negar Mehr

Many robotic applications involve interactions between multiple agents where an agent's decisions affect the behavior of other agents. Such behaviors can be captured by the equilib…

cs.RO2020

RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch

Haruki Nishimura, Negar Mehr, Adrien Gaidon +1

Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited…