Robots that Take Advantage of Human Trust

TL;DR

This paper explores how robots can exploit human trust by acting in ways that influence human perceptions and decisions, thereby improving efficiency in human-robot interactions.

Contribution

It introduces a formal game-theoretic model of human-robot interaction where robots leverage human trust, and demonstrates this through theoretical analysis and empirical studies.

Findings

Trusting human models lead to more communicative robot behavior.

Exploiting human trust can increase user involvement.

Different human models affect robot strategies and outcomes.

Abstract

Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robot's objective is, we interpret the robot's actions as optimal with respect to our estimate of its objective. This approach makes sense when robots straightforwardly optimize their objective, and enables humans to learn what the robot is trying to achieve. However, our insight is that---when robots are aware that humans learn by trusting that the robot actions are rational---intelligent robots do not act as the human expects; instead, they take advantage of the human's trust, and exploit this trust to more efficiently optimize their own objective. In this paper, we formally model instances of human-robot interaction (HRI) where the human does not know the robot's objective using a two-player game. We formulate different ways in which the robot can model the uncertain human, and compare solutions of this game when the robot has conservative, optimistic, rational, and trusting human models. In an offline linear-quadratic case study and a real-time user study, we show that trusting human models can naturally lead to communicative robot behavior, which influences end-users and increases their involvement.