Understanding Human-Robot Collaboration through Joint Action
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.
Authors
- Jingbo Wang (ORCID: https://orcid.org/0000-0002-5233-5211)
- Zhoutong Ye (ORCID: https://orcid.org/0009-0009-9929-4734)
- Yukun Wang
- Yuanchun Shi
- Chengwen Zhang
- Chang Liu
- Chun Yu
- Yuan Xu
- Jie Cai
Publication Details
- Journal
- arXiv (Cornell University)
- Published
- 2026-10-27
- DOI
- https://doi.org/10.48550/arxiv.2610.37298
- Primary Topic
- Social Robot Interaction and HRI
- Type
- preprint