AI & ML
Understanding Human-Robot Collaboration through Joint Action
arXiv cs.HC0 views
arXiv:2610.37298v1 Announce Type: new 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.
Source:https://arxiv.org/abs/2610.37298PreviousAn Interactive Mobile Augmented Reality System for Efficient Virtual Model-to-Patient Registration in Thoracic SurgeryNextComparing Temporal Deep Learning Architectures for Discrete Cross-User Myoelectric Control
Related posts
Launch a Business for Under $500: Real Odds, 4 Founders
0dev.to AI
Google, Microsoft, OpenAI and Anthropic Back a Shared AI Training Opt-Out Signal
0dev.to AI
Did AI Decode the Voynich Manuscript? Claims Tested
0dev.to AI
AIGB 解决 Teletype.in 群组管理痛点
0dev.to AI

Qual a melhor arquitetura de dados para a sua aplicação de IA?
0dev.to AI
Connect a Stateless Streamable HTTP MCP Server to Claude, ChatGPT, and Gemini for Tour Route Research
0dev.to WebDev