R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations

• URL: http://arxiv.org/abs/2510.18085v1
• Date: Mon, 20 Oct 2025 20:24:23 GMT
• Title: R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
• Authors: Connor Mattson, Varun Raveendra, Ellen Novoseller, Nicholas Waytowich, Vernon J. Lawhern, Daniel S. Brown,
• Abstract summary: We introduce and study Round-Robin Behavior Cloning (R2BC), a method that enables a single human operator to effectively train multi-robot systems.<n>Our approach allows the human to teleoperate one agent at a time and incrementally teach multi-agent behavior to the entire system.<n>We show that R2BC methods match, and in some cases surpass, the performance of an oracle behavior cloning approach trained on privileged synchronized demonstrations.
• Score: 8.790468078980306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-robot settings, relatively few studies have addressed the extension of these methods to multi-agent systems, especially in settings where a single human must provide demonstrations to a team of collaborating robots. In this paper, we introduce and study Round-Robin Behavior Cloning (R2BC), a method that enables a single human operator to effectively train multi-robot systems through sequential, single-agent demonstrations. Our approach allows the human to teleoperate one agent at a time and incrementally teach multi-agent behavior to the entire system, without requiring demonstrations in the joint multi-agent action space. We show that R2BC methods match, and in some cases surpass, the performance of an oracle behavior cloning approach trained on privileged synchronized demonstrations across four multi-agent simulated tasks. Finally, we deploy R2BC on two physical robot tasks trained using real human demonstrations.

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