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Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors.

Emma M van Zoelen1,2, Karel van den Bosch2, Matthias Rauterberg3

  • 1Interactive Intelligence, Intelligent Systems Department, Delft University of Technology, Delft, Netherlands.

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Summary

This study introduces a new method to observe unconscious human-robot team adaptations. It develops a language of interaction patterns to improve collaborative robot learning and awareness.

Keywords:
co-adaptationembodimentemergent interactionshuman-robot collaborationhuman-robot teaminteraction patterns

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Area of Science:

  • Human-Robot Interaction
  • Collaborative Robotics
  • Cognitive Science

Background:

  • Robots are becoming more integrated into human teams, requiring seamless adaptation to human behaviors for successful collaboration.
  • Many crucial human-robot adaptations occur through subtle, unconscious interactions that are difficult to observe and quantify.
  • Developing awareness of these co-adaptive behaviors is key to enhancing team learning and performance.

Purpose of the Study:

  • To develop an experimental paradigm for observing and understanding emergent human-robot co-adaptations.
  • To create a framework for awareness of co-adaptation that facilitates team learning in human-robot collaborations.
  • To derive a language of interaction patterns describing tacit co-adaptation in human-robot teams.

Main Methods:

  • Designed a physical human-robot collaborative task environment using a tangible interaction (a leash) to elicit unconscious adaptations.
  • Conducted a search-and-navigation task with 18 human participants collaborating with a robot.
  • Systematically annotated video recordings of participant-robot interactions to identify adaptive behaviors.

Main Results:

  • Identified four primary types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations, and active negotiations.
  • Developed a novel language of interaction patterns to describe tacit co-adaptation in human-robot collaborative contexts.
  • Demonstrated that the tangible interaction facilitated the expression of leading and following behaviors.

Conclusions:

  • The proposed paradigm and derived language of interaction patterns can effectively describe and enable awareness of implicit co-adaptation.
  • This framework can foster better communication and shared understanding between humans and robots in collaborative tasks.
  • Future studies can utilize this language to enhance robot learning and support human awareness of adaptive behaviors.