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To support positive, ethical human-robot interactions, robots need to be able to respond to unexpected situations in which societal norms are violated, including rejecting unethical commands. Implementing robust communication for robots is inherently difficult due to the variability of context in real-world settings and the risks of unintended influence during robots’ communication. HRI researchers have begun exploring the potential use of LLMs as a solution for language-based communication, which will require an in-depth understanding and evaluation of LLM applications in different contexts. In this work, we explore how an existing LLM responds to and reasons about a set of norm-violating requests in HRI contexts. We ask human participants to assess the performance of a hypothetical GPT-4-based robot on moral reasoning and explanatory language selection as it compares to human intuitions. Our findings suggest that while GPT-4 performs well at identifying norm violation requests and suggesting non-compliant responses, its flaws in not matching the linguistic preferences and context sensitivity of humans prevent it from being a comprehensive solution for moral communication between humans and robots. Based on our results, we provide a four-point recommendation for the community in incorporating LLMs into HRI systems. 

In this work, we present Robots for Social Justice (R4SJ): a framework for an equitable engineering practice of Human-Robot Interaction, grounded in the Engineering for Social Justice (E4SJ) framework for Engineering Education and intended to complement existing frameworks for guiding equitable HRI research. To understand the new insights this framework could provide to the feld of HRI, we analyze the past decade of papers published at the ACM/IEEE International Conference on Human-Robot Interaction, and examine how well current HRI research aligns with the principles espoused in the E4SJ framework. Based on the gaps identifed through this analysis, we make fve concrete recommendations, and highlight key questions that can guide the introspection for engineers, designers, and researchers. We believe these considerations are a necessary step not only to ensure that our engineering education eforts encourage students to engage in equitable and societally benefcial engineering practices (the purpose of E4SJ), but also to ensure that the technical advances we present at conferences like HRI promise true advances to society, and not just to fellow researchers and engineers. 

In this work, we present Robots for Social Justice (R4SJ): a framework for an equitable engineering practice of Human-Robot Interaction, grounded in the Engineering for Social Justice (E4SJ) framework for Engineering Education and intended to complement existing frameworks for guiding equitable HRI research. To understand the new insights this framework could provide to the field of HRI, we analyze the past decade of papers published at the ACM/IEEE International Conference on Human-Robot Interaction, and examine how well current HRI research aligns with the principles espoused in the E4SJ framework. Based on the gaps identified through this analysis, we make five concrete recommendations, and highlight key questions that can guide the introspection for engineers, designers, and researchers. We believe these considerations are a necessary step not only to ensure that our engineering education efforts encourage students to engage in equitable and societally beneficial engineering practices (the purpose of E4SJ), but also to ensure that the technical advances we present at conferences like HRI promise true advances to society, and not just to fellow researchers and engineers. 

Because robots are perceived as moral agents, they must behave in accordance with human systems of morality. This responsibility is especially acute for language-capable robots because moral communication is a method for building moral ecosystems. Language capable robots must not only make sure that what they say adheres to moral norms; they must also actively engage in moral communication to regulate and encourage human compliance with those norms. In this work, we describe four experiments (total N =316) across which we systematically evaluate two different moral communication strategies that robots could use to influence human behavior: a norm-based strategy grounded in deontological ethics, and a role-based strategy grounded in role ethics. Specifically, we assess the effectiveness of robots that use these two strategies to encourage human compliance with norms grounded in expectations of behavior associated with certain social roles. Our results suggest two major findings, demonstrating the importance of moral reflection and moral practice for effective moral communication: First, opportunities for reflection on ethical principles may increase the efficacy of robots’ role-based moral language; and second, following robots’ moral language with opportunities for moral practice may facilitate role-based moral cultivation. 

To enable robots to exert positive moral influence, we need to understand the impacts of robots’ moral communications, the ways robots can phrase their moral language to be most clear and persuasive, and the ways that these factors interact. Previous work has suggested, for example, that for certain types of robot moral interventions to be successful (i.e., moral interventions grounded in particular ethical frameworks), those interventions may need to be followed by opportunities for moral reflection, during which humans can critically engage with not only the contents of the robot’s moral language, but also with the way that moral language connects with their social-relational ontology and broader moral ecosystem. We conceptually replicate this prior work (N=119) using a design that more precisely manipulates moral ref lection. Our results confirm that opportunities for moral reflection are indeed critical to the success of robotic moral interventions—regardless of the ethical framework in which those interventions are grounded. 

Deployed social robots are increasingly relying on wakeword-based interaction, where interactions are human-initiated by a wakeword like “Hey Jibo”. While wakewords help to increase speech recognition accuracy and ensure privacy, there is concern that wakeword-driven interaction could encourage impolite behavior because wakeword-driven speech is typically phrased as commands. To address these concerns, companies have sought to use wake- word design to encourage interactant politeness, through wakewords like “⟨Name⟩, please”. But while this solution is intended to encourage people to use more “polite words”, researchers have found that these wakeword designs actually decrease interactant politeness in text-based communication, and that other wakeword designs could better encourage politeness by priming users to use Indirect Speech Acts. Yet there has been no previous research to directly compare these wakewords designs in in-person, voice-based human-robot interaction experiments, and previous in-person HRI studies could not effectively study carryover of wakeword-driven politeness and impoliteness into human-human interactions. In this work, we conceptually reproduced these previous studies (n=69) to assess how the wakewords “Hey ⟨Name⟩”, “Excuse me ⟨Name⟩”, and “⟨Name⟩, please” impact robot-directed and human-directed politeness. Our results demonstrate the ways that different types of linguistic priming interact in nuanced ways to induce different types of robot-directed and human-directed politeness. 

To enable robots to exert positive moral influence, we need to understand the impacts of robots’ moral communications, the ways robots can phrase their moral language to be most clear and persuasive, and the ways that these factors interact. Previous work has suggested, for example, that for certain types of robot moral interventions to be successful (i.e., moral interventions grounded in particular ethical frameworks), those interventions may need to be followed by opportunities for moral reflection, during which humans can critically engage with not only the contents of the robot’s moral language, but also with the way that moral language connects with their social-relational ontology and broader moral ecosystem. We conceptually replicate this prior work (N =119) using a design that more precisely manipulates moral reflection. Our results confirm that opportunities for moral reflection are indeed critical to the success of robotic moral interventions—regardless of the ethical framework in which those interventions are grounded. 

For enhanced performance and privacy, companies deploying voice-activated technologies such as virtual assistants and robots are increasingly tending toward designs in which technologies only begin attending to speech once a specified wakeword is heard. Due to concerns that interactions with such technologies could lead users, especially children, to develop impolite habits, some companies have begun to develop use modes in which interactants are required to use ostensibly polite wakewords such as " Please''. In this paper, we argue that these ``please-centering'' wakewords are likely to backfire and actually discourage polite interactions due to the particular types of lexical and syntactic priming induced by those wakewords. We then present the results of a human-subject experiment (n=90) that validates those claims. 

Most previous work on enabling robots’ moral competence has used norm-based systems of moral reasoning. However, a number of limitations to norm-based ethical theories have been widely acknowledged. These limitations may be addressed by role-based ethical theories, which have been extensively discussed in the philosophy of technology literature but have received little attention within robotics. My work proposes a hybrid role/norm-based model of robot cognitive processes including moral cognition.