Emergent Co-Adaptive Strategies in Heterogeneous Multi-Robot Systems Via Meta-Learning

Abstract

Abstract— As teamed robots increasingly share public spaces with humans, the ability to co-adapt—to mutually adjust behavior in response to one another—becomes essential for safe, efficient, and socially acceptable operation. This paper introduces a socially co-adaptive framework for heterogeneous multi-robot systems (HMRS) that enables real-time adaptation to human behavior while preserving cooperative task execution. Our approach fuses large language models for natural language understanding with model-agnostic meta-learning to allow robots to rapidly generalize across diverse social contexts. We implement and validate the system using a real-world HMRS composed of robots with different roles—workers, a station, and a social robot—interacting with 44 human participants under induced behavioral states (relaxed vs. nervous). Results reveal significant behavioral adaptation: the system dynamically shifts between egoistic and altruistic strategies, improving crowd guidance success by 21%. It also reduces human cognitive load—specifically, physical demands by 39% and temporal demands by 39%—while increasing trust by 16% and perceived anthropomorphism by 21%. This work demonstrates the feasibility of human-robot co-adaptation at scale, laying the groundwork for socially intelligent robotic systems capable of thriving in complex, human-centered environments.