Effect of Adapting to Human Preferences on Trust in Human-Robot Teaming

TL;DR

This paper investigates how a robot's ability to adapt to human preferences in real-time enhances trust in human-robot teams, using Bayesian Inverse Reinforcement Learning to improve interaction strategies.

Contribution

It introduces a novel human trust-behavior model and compares three interaction strategies, demonstrating the benefits of adaptive learning for trust enhancement.

Findings

Adaptive strategy yields highest trust levels.

Learning human preferences improves robot-human collaboration.

Real-time adaptation outperforms non-adaptive approaches.

Abstract

We present the effect of adapting to human preferences on trust in a human-robot teaming task. The team performs a task in which the robot acts as an action recommender to the human. It is assumed that the behavior of the human and the robot is based on some reward function they try to optimize. We use a new human trust-behavior model that enables the robot to learn and adapt to the human's preferences in real-time during their interaction using Bayesian Inverse Reinforcement Learning. We present three strategies for the robot to interact with a human: a non-learner strategy, in which the robot assumes that the human's reward function is the same as the robot's, a non-adaptive learner strategy that learns the human's reward function for performance estimation, but still optimizes its own reward function, and an adaptive-learner strategy that learns the human's reward function for performance estimation and also optimizes this learned reward function. Results show that adapting to the human's reward function results in the highest trust in the robot.