Back to the database
Record STMT-WD9AE63Voluntary

Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy

Report UnknownGlobaladopted

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.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

SpringerIndustry
Publisher
Bielefeld UniversityAcademic · DE
Lead issuer
Legal forceVoluntary
Under whose authorityUnknown
Judged fromfull text

Who it governs

Researchers and academicsGeneral publicAI developers and providersAI deployers and users

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.

Sectors
Research and science · human-robot interaction researchHealthcare · socially assistive robots for physical exercise

What the document contains

What's covered

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.

What's missing

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.

Key takeaways
  • 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.
Outline
1 Introduction
2 Adaptation, Automation and Alliance
3 System Design
4 Study Design
5 Results
6 Discussion
7 Conclusion
Issued8 February 2020
Length13,372 words · 17 pages
FormatNot recorded
Scopeglobal

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

0/5

Clear responsibility for AI outcomes

screening score · 2026-10-24

Fairness

0/5

Non-discrimination and equitable treatment

screening score · 2026-10-24

Human Dignity

0/5

Respect for the inherent worth of persons

screening score · 2026-10-24

Human Oversight

1/5

Humans 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

0/5

Protection of personal data and private life

screening score · 2026-10-24

Enforcement

0/5

Consequences and compliance mechanisms with real force

screening score · 2026-10-24

Impact Assessment

0/5

Required assessment of an AI system's risks/impacts before or during use

screening score · 2026-10-24

Topics

human_robot_interaction preference_learning adaptive_robots socially_assistive_robots user_experience