Good Robot, Bad Robot: Customer Responses to Norm-Compliant and Norm-Violating Service Robots

Service robots that interact with customers have penetrated various industries. With a basis in social identity theory, this study examines how customers respond to frontline service robots (FSRs) by investigating norm-compliant versus norm-violating behaviors compared with similar behaviors by human frontline employees (FLEs). In experimental studies, a black sheep effect occurs, such that customers downgrade norm-violating FLE behaviors more than similar behaviors by FSRs. They also upgrade norm-compliant behaviors by human FLEs more than those of FSRs. In service failures, this effect manifests as greater anger and frustration toward the FLE. We establish the underlying mechanism driving the black sheep effect: customers assign FSRs to an outgroup but categorize FLEs to their social ingroup, across different service encounters and independent of interaction frequency

Learning Multimodal Latent Dynamics for Human-Robot Interaction

This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We leverage the interaction dynamics learned from HHI to learn HRI and incorporate the conditional generation of robot motions from human observations into the training, thereby predicting more accurate robot trajectories. The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability. For contact-rich interactions, we modulate the robot's stiffness using HMM segmentation for a compliant interaction. We verify the effectiveness of our approach deployed on a Humanoid robot via a user study. Our method generalizes well to various humans despite being trained on data from just two humans. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines.Comment: 20 Pages, 10 Figure

One Size Does Not Fit All: Developing Robot User Types for Office Robots

Office robots can be a solution to the shortage of skilled workers in certain areas. They perform tasks automatically and work around the clock. Examples of tasks performed by these robots include data processing, clerical work, and administrative tasks. We propose five types of robot users based on interviews after real-life use cases of an office robot. We investigate these types in an online study that shows relevant patterns associated with each type and first indications of type distribution. By using these individual robot user types, organizations can tailor robot implementation to their workforce and create ideal human-robot interactions in the workplace

Good Robot, Bad Robot: Customer Responses to Norm-Compliant and Norm-Violating Service Robots

Service robots that interact with customers have penetrated various industries. With a basis in social identity theory, this study examines how customers respond to frontline service robots (FSRs) by investigating norm-compliant versus norm-violating behaviors compared with similar behaviors by human frontline employees (FLEs). In experimental studies, a black sheep effect occurs, such that customers downgrade norm-violating FLE behaviors more than similar behaviors by FSRs. They also upgrade norm-compliant behaviors by human FLEs more than those of FSRs. In service failures, this effect manifests as greater anger and frustration toward the FLE. We establish the underlying mechanism driving the black sheep effect: customers assign FSRs to an outgroup but categorize FLEs to their social ingroup, across different service encounters and independent of interaction frequency

You Got the Job! Understanding Hiring Decisions for Robots as Organizational Members

As social robots will likely be central to future human-robot interactions at work, we assess hiring decisions for social robots as a natural first step prior to their integration into organizations. With a basis in the technology acceptance model and social identity theory, this study focuses on differences between humanoid robotic, android robotic and human candidates. We first examine performance-based evaluations of the applicants by focusing on expectation disconfirmation. While for the human candidate, the interplay between expectations and experiences is decisive for the judgement, for social robots, the actual experience of the hiring situation dominates the decision. Besides the rational decision criteria, we further look into social-cue-based evaluations as social biases in hiring situations. Categorization as social ingroup leads to an absolute preference for the human candidate (i.e., ingroup favoritism) with no differences in preference for the robotic social outgroup (i.e., outgroup homogeneity effect)