Abstract

How can we effectively orchestrate large teams of robots and translate high-level goals into the nuanced local policies that guide individual robot behavior? Machine learning has revolutionized how we address these challenges, enabling the automatic synthesis of agent policies directly from task objectives. In this presentation, I will first describe how we use data-driven approaches to learn interaction strategies that foster coordination and cooperation within robot teams. I will then discuss methods for learning heterogeneous policies, where robots adopt different roles, and explain how this approach overcomes limitations inherent in traditional homogeneous models that force all robots to behave identically. Underpinning this work is a measure of ‘System Neural Diversity,’ a tool that allows us to quantify the degree of behavioral heterogeneity within multi-agent systems. I will demonstrate how this metric enables precise control over diversity in multi-robot tasks, leading to significant improvements in performance and efficiency, and unlocking the potential for novel and often surprising collective behaviors.