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Social Robot Navigation with Adaptive Proxemics Based on Emotions

Baris Bilen, Hasan Kivrak, Pinar Uluer, Hatice Kose

TL;DR

This work tackles safe and socially aware robot navigation by integrating emotion-driven proxemics into the navigation stack. It introduces a framework with leg-detection, emotion processing, and a four-layer social costmap, evaluated through ROS/Gazebo simulations and a 70-participant survey, with proxemic distances of $0.5$, $1.0$, and $1.5$ m corresponding to emotional states. Key findings show that adapting proxemics to emotion improves human safety and comfort: angry states require larger personal spaces, while happy states allow closer approaches without compromising safety, and public acceptance remains positive. The approach offers a practical path toward emotion-aware navigation in real-world human-robot interaction settings, with future work including real-time emotion detection and deployment on physical platforms such as the Pepper robot.

Abstract

The primary aim of this paper is to investigate the integration of emotions into the social navigation framework to analyse its effect on both navigation and human physiological safety and comfort. The proposed framework uses leg detection to find the whereabouts of people and computes adaptive proxemic zones based on their emotional state. We designed several case studies in a simulated environment and examined 3 different emotions; positive (happy), neutral and negative (angry). A survey study was conducted with 70 participants to explore their impressions about the navigation of the robot and compare the human safety and comfort measurements results. Both survey and simulation results showed that integrating emotions into proxemic zones has a significant effect on the physical safety of a human. The results revealed that when a person is angry, the robot is expected to navigate further than the standard distance to support his/her physiological comfort and safety. The results also showed that reducing the navigation distance is not preferred when a person is happy.

Social Robot Navigation with Adaptive Proxemics Based on Emotions

TL;DR

This work tackles safe and socially aware robot navigation by integrating emotion-driven proxemics into the navigation stack. It introduces a framework with leg-detection, emotion processing, and a four-layer social costmap, evaluated through ROS/Gazebo simulations and a 70-participant survey, with proxemic distances of , , and m corresponding to emotional states. Key findings show that adapting proxemics to emotion improves human safety and comfort: angry states require larger personal spaces, while happy states allow closer approaches without compromising safety, and public acceptance remains positive. The approach offers a practical path toward emotion-aware navigation in real-world human-robot interaction settings, with future work including real-time emotion detection and deployment on physical platforms such as the Pepper robot.

Abstract

The primary aim of this paper is to investigate the integration of emotions into the social navigation framework to analyse its effect on both navigation and human physiological safety and comfort. The proposed framework uses leg detection to find the whereabouts of people and computes adaptive proxemic zones based on their emotional state. We designed several case studies in a simulated environment and examined 3 different emotions; positive (happy), neutral and negative (angry). A survey study was conducted with 70 participants to explore their impressions about the navigation of the robot and compare the human safety and comfort measurements results. Both survey and simulation results showed that integrating emotions into proxemic zones has a significant effect on the physical safety of a human. The results revealed that when a person is angry, the robot is expected to navigate further than the standard distance to support his/her physiological comfort and safety. The results also showed that reducing the navigation distance is not preferred when a person is happy.
Paper Structure (17 sections, 3 figures, 3 tables)

This paper contains 17 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Framework
  4. Leg Detection
  5. Emotions
  6. Costmap Layers
  7. Global and Local Costmap
  8. Global and Local Planner
  9. Human Safety and Comfort Indices
  10. Experimental Studies and Results
  11. Simulation Study and Results
  12. Happy Emotion Simulations
  13. Angry Emotion Simulations
  14. NARS and Post-Test Questionnaire Results
  15. Negative Attitudes Toward Robots Scale (NARS)
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Navigation framework scheme for mobile robots. Consists of 2 parts; 1) Social aware navigation framework and 2) conventional navigation framework.
  • Figure 2: From left to right movement paths of the robot when the person is Happy (A), Neutral (B) or Angry (C)
  • Figure 3: These plots show the physiological safety of a person within different known and unknown emotions. Blue lines represent SII threshold value and red lines represents measured SII value.