Use of Winsome Robots for Understanding Human Feedback (UWU)

TL;DR

This paper investigates how the perceived cuteness of social robots influences human feedback in reinforcement learning, revealing biases and proposing an adaptive method to mitigate them.

Contribution

It introduces a study on the impact of robot aesthetics on human feedback and proposes an adaptive reinforcement learning approach to address feedback bias.

Findings

Cuteness affects the ratio of positive to negative feedback.

Humans tend to give more positive feedback to cuter robots.

An adaptive TAMER algorithm reduces feedback bias effects.

Abstract

As social robots become more common, many have adopted cute aesthetics aiming to enhance user comfort and acceptance. However, the effect of this aesthetic choice on human feedback in reinforcement learning scenarios remains unclear. Previous research has shown that humans tend to give more positive than negative feedback, which can cause failure to reach optimal robot behavior. We hypothesize that this positive bias may be exacerbated by the robot's level of perceived cuteness. To investigate, we conducted a user study where participants critique a robot's trajectories while it performs a task. We then analyzed the impact of the robot's aesthetic cuteness on the type of participant feedback. Our results suggest that there is a shift in the ratio of positive to negative feedback when perceived cuteness changes. In light of this, we experiment with a stochastic version of TAMER which adapts based on the user's level of positive feedback bias to mitigate these effects.