Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation

TL;DR

This paper introduces an online Bayesian method for improving human-robot teaming when both parties are suboptimal and have incomplete knowledge, leading to better performance and increased user trust and likeability.

Contribution

It develops a novel Bayesian approach for modeling and optimizing suboptimal human-robot collaboration with asymmetric capabilities and partial environmental knowledge.

Findings

Team performance improved significantly ($p<.001$)

User trust increased ($p<.001$)

Robot likeability perception enhanced ($p<.001$)

Abstract

For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly. Most prior works make the unrealistic assumption that one or more teammates can act near-optimally. In real-world collaboration, humans and autonomous agents can be suboptimal, especially when each only has partial domain knowledge. In this work, we develop computational modeling and optimization techniques for enhancing the performance of suboptimal human-agent teams, where the human and the agent have asymmetric capabilities and act suboptimally due to incomplete environmental knowledge. We adopt an online Bayesian approach that enables a robot to infer people's willingness to comply with its assistance in a sequential decision-making game. Our user studies show that user preferences and team performance indeed vary with robot intervention styles, and our approach for mixed-initiative collaborations enhances objective team performance ($p<.001$) and subjective measures, such as user's trust ($p<.001$) and perceived likeability of the robot ($p<.001$).