Do Mistakes Matter? Comparing Trust Responses of Different Age Groups to Errors Made by Physically Assistive Robots

TL;DR

The study investigates how intentional errors by physically assistive robots affect trust across younger and older adults during bathing and feeding tasks. Using two autonomous systems and a controlled error-injection protocol, the authors measure trust shifts with a modified HCT scale and perform thematic analysis on open-ended responses. Results show task- and age-dependent trust dynamics: younger adults exhibit measurable trust declines that may recover in bathing but persist in feeding, while older adults show no significant trust changes, with non-performance considerations driving their judgments. The findings imply that trust resilience to errors depends on task type and user experience, underscoring the need to account for user background and task context in the design of caregiving robotics.

Abstract

Trust is a key factor in ensuring acceptable human-robot interaction, especially in settings where robots may be assisting with critical activities of daily living. When practically deployed, robots are bound to make occasional mistakes, yet the degree to which these errors will impact a care recipient's trust in the robot, especially in performing physically assistive tasks, remains an open question. To investigate this, we conducted experiments where participants interacted with physically assistive robots which would occasionally make intentional mistakes while performing two different tasks: bathing and feeding. Our study considered the error response of two populations: younger adults at a university (median age 26) and older adults at an independent living facility (median age 83). We observed that the impact of errors on a users' trust in the robot depends on both their age and the task that the robot is performing. We also found that older adults tend to evaluate the robot on factors unrelated to the robot's performance, making their trust in the system more resilient to errors when compared to younger adults. Code and supplementary materials are available on our project webpage.

Figures (5)

• Figure 1: Top: Examples of intentional errors made by a Stretch RE1 robot performing a bathing task with younger participants. Bottom: Examples of intentional errors made by an Obi robot performing a feeding task with older participants.
• Figure 2: We designed a custom 3D-printed tool to allow the Stretch RE1 robot to hold a wet washcloth in its gripper.
• Figure 3: Summary time-series for successful trials and intentional error cases in the bathing (top) and feeding (bottom) tasks.
• Figure 4: For each set of trials across both tasks and participant populations, we present the composite scores in four trust subscales. The composite scores are given by the sum of all Likert item responses within each subscale. The Reliability, Understandability, and Technical Competence categories are on a scale of 0-10 while the Faith category is on a scale of 0-5. Statistically significant differences in the subscale scores between sets are denoted with an asterisk.
• Figure 5: Left Column: Pie charts representing the portion of individuals in each population who stated that they had previous experience with robots. Right Column: Pie charts representing the proportion of performance-based vs. non-performance-based statements made about the robot in each population.