Trust-Aware Decision Making for Human-Robot Collaboration
Abstract
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that integrates trust into robot decision making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 30, 2020
- Source ID
- 10.1145/3359616
Entities
People
- David Hsu
- Harold Soh
- Min Chen
- Siddhartha Srinivasa
- Stefanos Nikolaidis
Organizations
- Ministry of Education
- National Institutes of Health
- National Science Foundation
- National University of Singapore
- Office of Naval Research
- University of Southern California
- University of Washington