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

arXiv: https://arxiv.org/abs/2308.00945 · HTML: https://mlsanity.com/paper/2308.00945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [ToP-ToM: Trust-aware Robot Policy with Theory of Mind](https://mlsanity.com/paper/2311.04397/) (2023)

## Abstract (arXiv)

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.