The increasing human–robot interactions across service domains require an examination of their moral challenges, particularly from the user’s perspective. This study explores how efficiency and morality intersect in deploying service robots during the customer encounter. Utilizing a mixed-methods approach, we examine behavioral and attitudinal customer responses to service robot morality. In a field study, we uncover a tradeoff between efficiency-oriented and moral robots, varying based on product involvement (Study 1). Customers prefer moral robots for high-involvement products and efficiency-oriented robots for low-involvement products. In a subsequent laboratory study (Study 2), we focus on a high-involvement retail scenario to elucidate the underlying mechanisms of customer responses to moral service robots. We demonstrate that experienced robot morality benefits relational interactions by increasing customer trust. However, in transactional interactions where customers prioritize robot functionality, experienced robot morality can lead to drawbacks. By demonstrating the inadequacy of a one-size-fits-all approach to robot morality, we underscore the importance of adopting a customer-centric design for moral service robots and show that the effectiveness of their placement depends largely on the type of service encounter. These results can serve to guide managers and robot designers toward aligning robot morality with customer demands, facilitating positive human–robot interactions.

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. We additionally show the ability of our approach to generate successful interactions in a more complex scenario of Bimanual Robot-to-Human Handovers.

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).

One of the first and foremost non-verbal interactions that humans perform is a handshake. It has an impact on first impressions as touch can convey complex emotions. This makes handshaking an important skill for the repertoire of a social robot. In this paper, we present a novel framework for learning reaching behaviours for humanrobot handshaking behaviours for humanoid robots solely using third-person human-human interaction data. This is especially useful for non-backdrivable robots that cannot be taught by demonstrations via kinesthetic teaching. Our approach can be easily executed on different humanoid robots. This removes the need for re-training, which is especially tedious when training with human-interaction partners. We show this by applying the learnt behaviours on two different humanoid robots with similar degrees of freedom but different shapes and control limits.