Robot Vulnerability and the Elicitation of User Empathy

This paper describes a between-subjects Amazon Mechanical Turk study (n = 220) that investigated how a robot’s affective narrative influences its ability to elicit empathy in human observers. We first conducted a pilot study to develop and validate the robot’s affective narratives. Then, in the full study, the robot used one of three different affective narrative strategies (funny, sad, neutral) while becoming less functional at its shopping task over the course of the interaction. As the functionality of the robot degraded, participants were repeatedly asked if they were willing to help the robot. The results showed that conveying a sad narrative significantly influenced the participants’ willingness to help the robot throughout the interaction and determined whether participants felt empathetic toward the robot throughout the interaction. Furthermore, a higher amount of past experience with robots also increased the participants’ willingness to help the robot. This work suggests that affective narratives can be useful in short-term interactions that benefit from emotional connections between humans and robot

The Art of Inquiry: Toward Robots that Infer Speech and Movement Characteristics

To improve user personalization of robots in social situations, robots can benefit from inferring information about the humans with whom they interact. Physical human behaviors and personality traits have previously been touted as possible sources of such information but often require complex processing or sensoring requirements. This paper investigates posing specific questions related to extrovert behaviors as an alternative source of this information. It aims to highlight significant relationships between questions aimed at behavioral reactions in specific scenarios and speech and movement attributes, obtained by a robot in a one-on-one social interaction. The paper used an experiment in which participants interacted with a robot through a non-scripted conversation. In it, the robot would gather information on the speech and movement characteristics, and estimated arousal/valence levels of the participant. The experiment was followed by a series of specific questions aimed at outlining the extroversion level of the participants. The results showed multiple significant but weak correlations (p<.05) between the recorded attributes. These include correlations between the average determined valence and the average recorded velocity of speech, between the average answer reaction time and average answer length. The results also found correlations between arousal levels, average pause duration, and the answers recorded for individual questions of the questionnaire. The results suggest that introducing specific questions in human-robot interactions can potentially be used to decrease the processing and sensor demands of robots and offer user personalization using only a limited set of sensors

Affecta-context: The Context-Guided Behavior Adaptation Framework

This paper presents Affecta-context, a general framework to facilitate behavior adaptation for social robots. The framework uses information about the physical context to guide its behaviors in human-robot interactions. It consists of two parts: one that represents encountered contexts and one that learns to prioritize between behaviors through human-robot interactions.As physical contexts are encountered the framework clusters them by their measured physical properties. In each context, the framework learns to prioritize between behaviors to optimize the physical attributes of the robot's behavior in line with its current environment and the preferences of the users it interacts with.This paper illlustrates the abilities of the Affecta-context framework by enabling a robot to autonomously learn the prioritization of discrete behaviors. This was achieved by training across 72 interactions in two different physical contexts with 6 different human test participants. The paper demonstrates the trained Affecta-context framework by verifying the robot's ability to generalize over the input and to match its behaviors to a previously unvisited physical context