Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy
This academic paper investigates the effects of different levels of automation in socially assistive robots during physical exercise sessions by comparing an adaptive robot using online preference learning with an adaptable robot directly controlled by the user. The authors conducted a between-subject study with 40 participants, evaluating the impact of these personalization mechanisms on perceived competence, warmth, trust in the alliance, and user motivation. The findings indicate that users evaluate adaptive robots as significantly more competent and warm, report a higher working alliance, and that perceived competence statistically mediates this improved alliance.
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Who issued it
Who it governs
Researchers and developers in human-robot interaction and assistive robotics should read this to understand how different levels of automation and preference learning impact user trust, competence perception, and alliance.
What the document contains
The document covers the design and evaluation of a socially assistive robot system that uses an online preference learning algorithm (Double Thompson Sampling) to adapt exercise routines to user preferences, comparing its psychological and behavioral effects against a user-controlled adaptable robot.
The text does not provide regulatory compliance guidelines, legal frameworks for autonomous systems, or enforcement mechanisms, as it is a scientific research study focused exclusively on human-robot interaction and preference learning.
- Users perceive fully adaptive socially assistive robots as significantly more competent and warm compared to adaptable, user-controlled systems.
- Trust in the working alliance with an adaptive robot is rated higher than with an adaptable robot.
- The positive effect of system autonomy on the working alliance is significantly mediated by the perceived competence of the system.
- The k-armed Dueling Bandit Problem using Double Thompson Sampling is demonstrated to be a suitable and effective approach for online preference learning in human-robot interaction scenarios.
- Participants exhibited different qualitative selection strategies, focusing on novelty when the system was adaptable and on current enjoyment when the system was adaptive.
1 Introduction 2 Adaptation, Automation and Alliance 3 System Design 4 Study Design 5 Results 6 Discussion 7 Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
1/5Humans remain in control of consequential decisions
Either the user controls the interaction, or the robot is in control.
Abstract
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-24
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-24