Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity. Designing for joint activity focuses on explicitly supporting the interdependencies between agents necessary for effective coordination among agents engaged in the joint activity. This builds and expands upon designing for usability to further address how technologies can be designed to act as effective team players. Effective joint activity requires supporting, at minimum, five primary macrocognitive functions within teams: Event Detection, Sensemaking, Adaptability, Perspective-Shifting, and Coordination. Supporting these functions is equally as important as making technologies usable. We synthesized fourteen heuristics from relevant literature including display design, human factors, cognitive systems engineering, cognitive psychology, and computer science to aid the design, development, and evaluation of technologies that support joint human-machine activity.
A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance
Socially aware robots should be able, among others, to support fluent human-robot collaboration in tasks that require interdependent actions in order to be solved. Towards enhancing mutual performance, collaborative robots should be equipped with adaptation and learning capabilities. However, co-learning can be a time consuming procedure. For this reason, transferring knowledge from an expert could potentially boost the overall team performance. In the present study, transfer learning was integrated in a deep Reinforcement Learning (dRL) agent. In a real-time and real-world set-up, two groups …
As the use of Augmented Reality (AR) to enhance interactions between human agents and robotic systems in a work environment continues to grow, robots must communicate their intents in informative yet straightforward ways. This improves the human agent's feeling of trust and safety in the work environment while also reducing task completion time. To this end, we discuss a set of guidelines for the systematic design of AR interfaces for Human-Robot Interaction (HRI) systems. Furthermore, we develop design frameworks that would ride on these guidelines and serve as a base for researchers seeking …
Enhancing Human-Robot Collaboration through Existing Guidelines: A Case Study Approach
Nagoya University（名古屋大学）；IMAGINARY Corporation（IMAGINARY公司）；Atelier Corporation（Atelier公司）；National Institute of Advanced Industrial Science and Technology (AIST)（独立行政法人产业技术综合研究所（AIST））
PerspAct: Enhancing LLM Situated Collaboration Skills through Perspective Taking and Active Vision
University of Milan-Bicocca（米兰-比科卡大学）；University of Naples Federico II（那不勒斯费德里科二世大学）；Oversonic Robotics（Oversonic机器人公司）；University of Essex（埃塞克斯大学）；TUM School of Social Sciences and Technology（慕尼黑技术大学社会科学与技术学院）
Personalized AI Scaffolds Synergistic Multi-Turn Collaboration in Creative Work
D’Amore-McKim School of Business, Northeastern University（东北大学达莫尔-麦金商学院）；Network Science Institute, Northeastern University（东北大学网络科学研究所）；Khoury College of Computer Sciences, Northeastern University（东北大学库雷计算机科学学院）
