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Vibrotactile information coding strategies for a body-worn vest to aid robot-human collaboration

Adrian Vecina Tercero, Praminda Caleb-Solly

TL;DR

This work addresses how to convey real-time robot information to a human operator in cognitively demanding USAR settings using a body-worn vibrotactile vest. It compares semantic vibrotactile messages, which map events to skin-like shapes, against traditional positional patterns using a large 40-motor matrix, and evaluates learnability, accuracy, and resilience to mental workload. Semantics-based patterns yield higher identification accuracy ($79.18\%$) than positional patterns ($63.70\%$, $p=0.027$) and show minimal performance degradation under added cognitive load ($p=0.013$ for the positional group). The findings suggest semantic vibrotactile coding as a promising approach for richer, more intuitive robot-to-human communication in HRC, with practical implications for PPE integration and real-world deployment, albeit requiring further validation with rescuers and in motion.

Abstract

This paper explores the use of a body-worn vibrotactile vest to convey real-time information from robot to operator. Vibrotactile communication could be useful in providing information without compropmising or loading a person's visual or auditory perception. This paper considers applications in Urban Search and Rescue (USAR) scenarios where a human working alongside a robot is likely to be operating in high cognitive load conditions. The focus is on understanding how best to convey information considering different vibrotactile information coding strategies to enhance scene understanding in scenarios where a robot might be operating remotely as a scout. In exploring information representation, this paper introduces Semantic Haptics, using shapes and patterns to represent certain events as if the skin was a screen, and shows how these lead to bettter learnability and interpreation accuracy.

Vibrotactile information coding strategies for a body-worn vest to aid robot-human collaboration

TL;DR

This work addresses how to convey real-time robot information to a human operator in cognitively demanding USAR settings using a body-worn vibrotactile vest. It compares semantic vibrotactile messages, which map events to skin-like shapes, against traditional positional patterns using a large 40-motor matrix, and evaluates learnability, accuracy, and resilience to mental workload. Semantics-based patterns yield higher identification accuracy () than positional patterns (, ) and show minimal performance degradation under added cognitive load ( for the positional group). The findings suggest semantic vibrotactile coding as a promising approach for richer, more intuitive robot-to-human communication in HRC, with practical implications for PPE integration and real-world deployment, albeit requiring further validation with rescuers and in motion.

Abstract

This paper explores the use of a body-worn vibrotactile vest to convey real-time information from robot to operator. Vibrotactile communication could be useful in providing information without compropmising or loading a person's visual or auditory perception. This paper considers applications in Urban Search and Rescue (USAR) scenarios where a human working alongside a robot is likely to be operating in high cognitive load conditions. The focus is on understanding how best to convey information considering different vibrotactile information coding strategies to enhance scene understanding in scenarios where a robot might be operating remotely as a scout. In exploring information representation, this paper introduces Semantic Haptics, using shapes and patterns to represent certain events as if the skin was a screen, and shows how these lead to bettter learnability and interpreation accuracy.

Paper Structure

This paper contains 18 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Vibrotactile Haptics
  4. Vibrotactile Feedback in Robotics
  5. Vibrotactile Message Modalities
  6. In the Context of this study
  7. Methods
  8. Evaluation Methods
  9. Results and Analysis
  10. Identification Accuracy and Selection Delay
  11. Comparing Patterns
  12. Path Similarity
  13. Questionnaire Answers and Participant Feedback
  14. Discussion
  15. Vibrotactile Messages for Information Delivery
  16. ...and 3 more sections

Figures (8)

  • Figure 1: UI used for training and testing
  • Figure 2: TactVest X40, motors exposed
  • Figure 3: Top-Down representation of the band of motors used for directions. Motors $mf_0, mf_3, mb_0$ are active
  • Figure 4: Top-Down representation of Spot Robot, along with displayed haptic direction
  • Figure 5: Identification Accuracy for both groups, with and without additional mental workload (MWL). The semantic pattern group performs significantly better
  • ...and 3 more figures