Your Robot is Watching: Using Surface Cues to Evaluate the Trustworthiness of Human Actions A number of important humanrobot applications demand trust. Although a great deal of research has examined how and why people trust robots, less work has explored how robots might decide whether to trust humans. Surface cues are perceptual clues that provide hints as to a personxe2x80x99s intent and are predictive of behavior. This paper proposes and evaluates a model for recognizing trust surface cues by a robot and predicting if a personxe2x80x99s behavior is deceitful in the context of a trust game. The model was tested in simulation and on a physical robot that plays an interactive card game. A human study was conducted where subjects played the game against a simulation, the robot, and a human opponent. Video data was hand coded by two coders with an interrater reliability of 0.41 based on Levenshtein distance. It was found that the model outperformedmatched the human coders on 50% of the subjects. Overall, this paper contributes a method that may begin to allow robots to evaluate the surface cues generated by a person to determine whether or not it should trust them.

Robots that Take Advantage of Human Trust Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robotxe2x80x99s objective is, we interpret the robotxe2x80x99s 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 thatxe2x80x93when robots are aware that humans learn by trusting that the robot actions are rationalxe2x80x93intelligent robots do not act as the human expects; instead, they take advantage of the humanxe2x80x99s trust, and exploit this trust to more efficiently optimize their own objective. In this paper, we formally model instances of humanrobot interaction (HRI) where the human does not know the robotxe2x80x99s objective using a twoplayer 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 linearquadratic case study and a realtime user study, we show that trusting human models can naturally lead to communicative robot behavior, which influences endusers and increases their involvement.