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Identifying Influential Actions in Human-Robot Interactions

Haoyang Jiang, Chenfei Xu, Yuya Okadome, Yukata Nakamura

TL;DR

This paper introduces a method for identifying influential robot actions using transfer entropy, a statistic that measures directed information transfer between time series, and demonstrates TE's capability to identify key actions influencing human responses, highlighting its potential to improve the design and adaptability of robotic systems.

Abstract

Human-robot interaction combines robotics, cognitive science, and human factors to study collaborative systems. This paper introduces a method for identifying influential robot actions using transfer entropy, a statistic that measures directed information transfer between time series. TE is effective for capturing complex, nonlinear interactions. We apply this method to analyze how robot actions affect human behavior during a conversation with a remotely controlled robot avatar. By focusing on the impact of proximity, our approach demonstrates TE's capability to identify key actions influencing human responses, highlighting its potential to improve the design and adaptability of robotic systems.

Identifying Influential Actions in Human-Robot Interactions

TL;DR

This paper introduces a method for identifying influential robot actions using transfer entropy, a statistic that measures directed information transfer between time series, and demonstrates TE's capability to identify key actions influencing human responses, highlighting its potential to improve the design and adaptability of robotic systems.

Abstract

Human-robot interaction combines robotics, cognitive science, and human factors to study collaborative systems. This paper introduces a method for identifying influential robot actions using transfer entropy, a statistic that measures directed information transfer between time series. TE is effective for capturing complex, nonlinear interactions. We apply this method to analyze how robot actions affect human behavior during a conversation with a remotely controlled robot avatar. By focusing on the impact of proximity, our approach demonstrates TE's capability to identify key actions influencing human responses, highlighting its potential to improve the design and adaptability of robotic systems.
Paper Structure (13 sections, 5 equations, 10 figures)

This paper contains 13 sections, 5 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Human-Robot Interactions
  4. Transfer Entropy
  5. Experiments
  6. Methodology
  7. Transfer Entropy
  8. Influence of Actions
  9. Workflow
  10. Model Structure and Training
  11. Compensation for Relative Measurements
  12. Results
  13. Conclusion

Figures (10)

  • Figure 1: Human-Robot Avatar conversation.
  • Figure 2: The robot avatar.
  • Figure 3: Depth processing.
  • Figure 4: Left: Non-influential actions; Right: Influential actions.
  • Figure 5: The workflow of the proposed method. Top: Visualisation of the timeline. Bottom: Illustration of the workflow.
  • ...and 5 more figures