Reward Shaping for Building Trustworthy Robots in Sequential Human-Robot Interaction
This research paper addresses the trade-off in human-robot interaction where maximizing task rewards can reduce human trust, while prioritizing trust can compromise performance. The authors formulate sequential human-robot interaction as a two-player Markov game and use a potential-based reward-shaping technique to improve human trust with a bounded performance loss. Applied to an experience-based trust model, the framework yields a linear program that is evaluated in a simulated search-and-rescue mission.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and robotics developers looking to model and optimize human trust and task performance in human-robot teams.
What the document contains
The document covers a computational framework for balancing task performance and human trust in sequential human-robot interaction using potential-based reward shaping and an experience-based trust dynamics model.
The text does not provide a necessary condition for guaranteeing small performance loss, nor does it evaluate potential functions other than linear forms.
- Maximizing task rewards in human-robot interaction often decreases human trust, whereas maximizing trust compromises performance.
- The sequential human-robot interaction process can be effectively modeled as a two-player finite-horizon Markov game.
- Potential-based reward shaping can guide a robot to increase human trust while mathematically bounding task performance loss.
- Applying this framework to an experience-based trust model results in an efficiently solvable linear program.
- Simulations of a search-and-rescue mission demonstrate that the proposed method successfully increases human trust at a minimal performance cost.
· I. Introduction · II. Related Work · III. Problem Formulation · IV. Reward Shaping for Trustworthy Policy · V. Case Study · VI. 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 · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-23