In this paper, we consider the problem of discovering dynamical system m...
We present a novel algorithm for motion planning in complex, multi-agent...
Connected autonomous vehicles (CAVs) promise to enhance safety, efficien...
Game-theoretic inverse learning is the problem of inferring the players'...
We focus on developing efficient and reliable policy optimization strate...
Contingency planning, wherein an agent generates a set of possible plans...
Markov games model interactions among multiple players in a stochastic,
...
To provide safe and efficient services, robots must rely on observations...
Robots deployed to the real world must be able to interact with other ag...
In multi-agent dynamic games, the Nash equilibrium state trajectory of e...
An atomic routing game is a multiplayer game on a directed graph. Each p...
Modern robots require accurate forecasts to make optimal decisions in th...
Saddle-point problems appear in various settings including machine learn...
In an inverse game problem, one needs to infer the cost function of the
...
Interactions among multiple self-interested agents may not necessarily y...
In multi-agent settings, game theory is a natural framework for describi...
We study the problem of autonomous racing amongst teams composed of
coop...
Connected and Autonomous Vehicles (CAVs) are becoming more widely deploy...
Robots operating in complex, multi-player settings must simultaneously m...
We develop a hierarchical controller for multi-agent autonomous racing. ...
We study the class of reach-avoid dynamic games in which multiple agents...
Robots and autonomous systems must interact with one another and their
e...
We present the concept of a Generalized Feedback Nash Equilibrium (GFNE)...
We present a novel method for handling uncertainty about the intentions ...
Robots deployed in real-world environments should operate safely in a ro...
In this paper, we present a method for finding approximate Nash equilibr...
This paper proposes a framework for adaptively learning a feedback
linea...
In many settings where multiple agents interact, the optimal choices for...
We present a novel approach to control design for nonlinear systems, whi...
Iterative linear-quadratic (ILQ) methods are widely used in the nonlinea...
Differential games offer a powerful theoretical framework for formulatin...
We present a new framework for motion planning that wraps around existin...
Robust motion planning is a well-studied problem in the robotics literat...
We study the adaptive sensing problem for the multiple source seeking
pr...
The implementation of optimal power flow (OPF) methods to perform voltag...
In order to safely operate around humans, robots can employ predictive m...
Hamilton-Jacobi (HJ) reachability analysis has been developed over the p...
Motion planning is an extremely well-studied problem in the robotics
com...
Learning cooperative policies for multi-agent systems is often challenge...