Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning
Open Access
- 6 July 2021
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Robotics and AI
Abstract
Becoming a well-functioning team requires continuous collaborative learning by all team members. This is calledco-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.This publication has 22 references indexed in Scilit:
- Robot education peers in a situated primary school study: Personalisation promotes child learningPLOS ONE, 2017
- Human-Robot Mutual Adaptation in Shared AutonomyPublished by Association for Computing Machinery (ACM) ,2017
- Human-robot mutual adaptation in collaborative tasks: Models and experimentsThe International Journal of Robotics Research, 2017
- Coactive Design: Designing Support for Interdependence in Joint ActivityJournal of Human-Robot Interaction, 2014
- Co-constructing Grounded Symbols—Feedback and Incremental Adaptation in Human–Agent DialogueKI - Künstliche Intelligenz, 2013
- Reinforcement Learning and Markov Decision ProcessesPublished by Springer Science and Business Media LLC ,2012
- Formation conditions of mutual adaptation in human-agent collaborative interactionApplied Intelligence, 2010
- Understanding team adaptation: A conceptual analysis and model.Journal of Applied Psychology, 2006
- Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent ActivityIEEE Intelligent Systems, 2004
- Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learningArtificial Intelligence, 1999