A Survey of Multi-Agent Human-Robot Interaction Systems
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Abstract (arXiv)
This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2022.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 2.
10 papers.
- 2024To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactionscited by 10
- 2024Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentationcited by 6
- 2023Utility Theory based Cognitive Modeling in the Application of Robotics: A Surveycited by 3
- 2025Boosting Team Modeling through Tempo-Relational Representation Learningcited by 3
- 2026Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperationcited by 3
- 2026PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interactioncited by 3
- 2024Software Engineering for Collective Cyber-Physical Ecosystemscited by 2
- 2025Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Modelscited by 1
- 2024Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learningcited by 0
- 2025Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environmentscited by 0
- 2026PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction9 Apr 2026
- 2026Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation22 Feb 2026
- 2025Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models27 Oct 2025
- 2025Boosting Team Modeling through Tempo-Relational Representation Learning17 Jul 2025
- 2025Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments16 Apr 2025
- 2024Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning2 Oct 2024
- 2024Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentation2 Oct 2024
- 2024Software Engineering for Collective Cyber-Physical Ecosystems7 Jun 2024
- 2024To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions19 Mar 2024
- 2023Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey15 Jun 2023