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

TL;DR

This paper introduces a novel potential-minimizing control approach for multi-agent trajectory planning, simplifying the complex differential game equilibrium computation into a single optimal control problem, enabling scalable and real-time multi-agent interaction planning.

Contribution

It proposes a new potential-based optimal control framework for multi-agent trajectory planning, leveraging the structure of potential differential games for computational efficiency.

Findings

Outperforms state-of-the-art game solvers in simulations

Demonstrates real-time planning for two quadcopters

Shows scalability to multi-agent interactions

Abstract

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 equilibria of differential games which provide an expressive framework for modeling the agents' mutual influence. However, finding the equilibria of differential games is in general challenging as it involves solving a set of coupled optimal control problems. In this work, we propose to leverage the special structure of multi-agent interactions to generate interactive trajectories by simply solving a single optimal control problem, namely, the optimal control problem associated with minimizing the potential function of the differential game. Our key insight is that for a certain class of multi-agent interactions, the underlying differential game is indeed a potential differential game for which equilibria can be found by solving a single optimal control problem. We introduce such an optimal control problem and build on single-agent trajectory optimization methods to develop a computationally tractable and scalable algorithm for planning multi-agent interactive trajectories. We will demonstrate the performance of our algorithm in simulation and show that our algorithm outperforms the state-of-the-art game solvers. To further show the real-time capabilities of our algorithm, we will demonstrate the application of our proposed algorithm in a set of experiments involving interactive trajectories for two quadcopters.