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arXiv · 2610.37298

Understanding Human-Robot Collaboration through Joint Action

Chengwen Zhang·Jianyang Wang·Chang Liu·Yukun Wang·Yuan Xu·Zhoutong Ye·Jie Cai·Chun Yu·Yuanchun Shi
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Abstract

Human-robot collaboration requires partners to coordinate interdependent contributions toward a shared goal. Joint Action theory provides concepts for understanding this coordination, but how these concepts are instantiated and combined across empirical research remains fragmented. We conducted a systematic review following PRISMA 2020, identifying 83 empirical papers published between 2021 and 2026. Through theory-driven framework analysis, we organized the literature into nine constructs across three capacities: Representation, Prediction, and Integration. We analyze how these constructs are distributed and co-occur across studies, revealing where empirical evidence concentrates and where it is absent. By mapping empirical HRC research onto a unified Joint Action framework, this review offers a coherent overview of HRC studies, identifies gaps in current evidence, and provides guidelines for future research.

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Chengwen Zhang, Jianyang Wang, Chang Liu, Yukun Wang, Yuan Xu, Zhoutong Ye, Jie Cai, Chun Yu, Yuanchun Shi. 2026-10-27. Understanding Human-Robot Collaboration through Joint Action. https://arxiv.org/abs/2610.37298

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