Reward Shaping for Building Trustworthy Robots in Sequential Human-Robot Interaction

TL;DR

This paper introduces a reward-shaping framework for human-robot interaction that balances increasing human trust with maintaining task performance, using a Markov game approach and potential-based rewards.

Contribution

It formulates trust-aware HRI as a Markov game and applies potential-based reward shaping to enhance trust without significant performance loss.

Findings

Successfully increased human trust in simulation

Bounded performance loss with potential-based rewards

Efficient linear program for real-world deployment

Abstract

Trust-aware human-robot interaction (HRI) has received increasing research attention, as trust has been shown to be a crucial factor for effective HRI. Research in trust-aware HRI discovered a dilemma -- maximizing task rewards often leads to decreased human trust, while maximizing human trust would compromise task performance. In this work, we address this dilemma by formulating the HRI process as a two-player Markov game and utilizing the reward-shaping technique to improve human trust while limiting performance loss. Specifically, we show that when the shaping reward is potential-based, the performance loss can be bounded by the potential functions evaluated at the final states of the Markov game. We apply the proposed framework to the experience-based trust model, resulting in a linear program that can be efficiently solved and deployed in real-world applications. We evaluate the proposed framework in a simulation scenario where a human-robot team performs a search-and-rescue mission. The results demonstrate that the proposed framework successfully modifies the robot's optimal policy, enabling it to increase human trust at a minimal task performance cost.