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Robot Vulnerability and the Elicitation of User Empathy

Morten Roed Frederiksen, Katrin Fischer, Maja Matarić

TL;DR

The paper investigates whether robot-driven affective narratives can elicit empathy and sustain human help in short-term interactions where a robot degrades at a task. Using a between-subject AMT design, it compares funny, sad, and neutral narratives across a 24-step grocery-shopping task, measuring willingness to help and an empathy index. The key finding is that a sad narrative significantly increases sustained willingness to help, though it does not increase overall empathy scores; humor shows limited impact. Importantly, participants' prior familiarity with robots predicts both helping behavior and self-reported empathy, highlighting the role of user experience in shaping HRI outcomes. The work demonstrates that carefully crafted, first-person-like narratives can catalyze emotional engagement in brief human–robot interactions, with practical implications for SAR design and real-world deployments.

Abstract

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot's affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot's affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants' willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants' willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.

Robot Vulnerability and the Elicitation of User Empathy

TL;DR

The paper investigates whether robot-driven affective narratives can elicit empathy and sustain human help in short-term interactions where a robot degrades at a task. Using a between-subject AMT design, it compares funny, sad, and neutral narratives across a 24-step grocery-shopping task, measuring willingness to help and an empathy index. The key finding is that a sad narrative significantly increases sustained willingness to help, though it does not increase overall empathy scores; humor shows limited impact. Importantly, participants' prior familiarity with robots predicts both helping behavior and self-reported empathy, highlighting the role of user experience in shaping HRI outcomes. The work demonstrates that carefully crafted, first-person-like narratives can catalyze emotional engagement in brief human–robot interactions, with practical implications for SAR design and real-world deployments.

Abstract

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot's affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot's affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants' willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants' willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robots.
Paper Structure (15 sections, 4 figures)

This paper contains 15 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Previous approaches
  3. Empathy in HRI
  4. Empathy elicited by the narrative
  5. Method
  6. Study Design
  7. Participants & Measures
  8. Procedure
  9. Results
  10. Pilot Study
  11. Pilot Study Results
  12. Full Study Design
  13. Study Results
  14. Discussion
  15. Conclusion

Figures (4)

  • Figure 1: A single step of the AMT study.
  • Figure 2: Flowchart of administered study questionnaires.
  • Figure 3: Plot of the drop-off rate of participants over the course of the 14-step pilot study for the three conditions. The drop-off is plotted as the percentage of remaining participants at each interaction step, defined as the share of participants who have agreed to help the robot at any previous step.
  • Figure 4: Plot of the drop-off rate of participants over the course of the 24-step pilot study for the three conditions. The drop-off is plotted as the percentage of remaining participants at each interaction step, defined as the share of participants who have agreed to help the robot at any previous step. This figure only depicts the participants’ initial refusal to aid the robot and it does not reflect the data for those who re-engaged with the robot.