Proactive collaboration via autonomous interaction

Human teams excel at dynamically restructuring both task assignments and team composition in response to emerging challenges, proactively recruiting or releasing members as needed. This capacity for autonomous adaptation is a cornerstone of effective teamwork, yet remains difficult to achieve in heterogeneous multi-robot systems, which typically operate under fixed team configurations or adapt only responsively to external disruptions. In this work, we present a systematic investigation of the Proactive Collaboration paradigm for robot teams, where the working team autonomously recruits or releases members as tasks evolve. We implement this paradigm by equipping robots with the developed Autonomous Interaction framework, which utilizes need-driven multi-round communication to facilitate discussions over task progress, negotiated task allocation, and dynamic team resizing. Through real-world and simulated experiments, we demonstrate that our framework effectively realizes the Proactive Collaboration. By resolving capability gaps via anticipatory planning and minimizing action redundancy, it yields consistent and measurable gains in team efficiency and robustness. Our findings suggest that enabling individual-level initiative may offer a promising pathway toward more adaptive and cohesive collective behavior in multi-robot systems.