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This chapter seeks to enable uninformed users to instruct intelligent robots to move towards specific goals via route instructions. Empirical data are used to augment the robot's internal representation and to identify conceptual problem areas. A formal dialogue-modelling approach is suggested, supporting the design of robust dialogue systems.

This chapter projects possible human futures with robot sex workers (RSWs). These are models of robots with integrated capabilities designed to enhance human sexual pleasure. Using attachment theory as a framework, concrete questions about what might constitute human-robot sexual and emotionally intimate relationships are presented for discussion, including design and cultural issues. Mori’s Uncanny Valley theory is used as a springboard for a new model to include temporal and cultural influences on people who interact with robots, the Robot Accommodation Process Theory (RAPT). Finally, the ideas of how RSW will be normalized into individual human-robot relationships as well as how the relationships will be valued by culture over time are used as a premise for building theory in the areas of meaningful and affectionate human-robot relationships.

The concept of trust can take various forms, from interpersonal trust to institutional trust to trust in oneself or one’s government. As robotic technologies approach autonomy, and in increasing cases achieve it, scholars have turned their attention to the relationship between trust and human–robot interactions. This chapter explores that relationship using a multidisciplinary approach that includes philosophy, law, and neuroscience. The first section explicates the concept of human–robot interaction. The second articulates a normative account of interpersonal trust in service of the third section’s exploration of whether human–robot interactions could approach or achieve interpersonal trust. In answering this question in the affirmative, the fourth section flags some of the potential deleterious consequences of facilitating interpersonal trust in human–robot interactions. The fifth concludes with a call for future scholarship to address the philosophical, empirical, legal, and policy issues related to trust in human–robot interactions.

From a traditional engineering perspective, communication is about effecting control over a distance, and its primary concern is the reliability of transmission. This chapter reviews communication in nature, describing its evolution from the perspective of the selfish gene. Communication in nature is ubiquitous and generally honest, and arises as much from collaboration as manipulation. We show that context and relevance allow effective communication with little information transfer, particularly between organisms with similar capacities and goals. Human language differs fundamentally from the non-verbal communication we share with other animals; robots may need to accommodate both. We document progress in AI capacities to generate synthetic emotion and to sense and classify human emotion. Communication in contemporary biomimetic systems is between robots in swarm robotics, but also between robot and human for both autonomous and collaborative systems. We suggest increased future emphasis on capacities to receive and comprehend signs, and on the pragmatic utility of communication and cooperation.

People have a tendency to project lifelike qualities onto robots. As we increasingly create spaces where robotic technology interacts with humans, this inclination raises ethical questions about use and policy. An experiment conducted in our lab on human–robot interaction indicates that framing robots through anthropomorphic language (like a personified name or story) can impact how people perceive and treat a robot. This chapter explores the effects of encouraging or discouraging people to anthropomorphize robots through framing. I discuss concerns about anthropomorphizing robotic technology in certain contexts, but I argue that there are also cases where encouraging anthropomorphism is desirable. Because people respond to framing, framing could help to separate these cases.

Previous research on spatial projective terms such as to the left (of) and in front (of) typically focuses on static (locative) usages. This chapter addresses the usage of such expressions in dynamic contexts (i.e. as directionals). As part of the general aim of developing a speech interface for allowing intuitive control of a mobile robot in navigation tasks, this chapter describes the implementation of a carefully selected subset of interpretations of directional terms in a robotic system. The system was validated and gradually im­proved by experiments involving spontaneous directional instruc­tions by users who were not informed about the robot’s capabilities.

Robots are increasingly a part of children’s lives—teaching in classrooms, comforting children in hospitals, and playing in their homes. This chapter reviews literature on children’s understanding and trust of robots, including the authors’ own emerging research addressing these topics empirically. It demonstrates that children’s understanding of the abilities and behaviors of robots affects whether children like and are willing to learn from robots. The chapter emphasizes that children’s beliefs about the psychological, social, and perceptual abilities of robots change with age and differentially impact children’s feelings toward and their willingness to learn from them. Empirical research addressing these issues is in its infancy, so the chapter concludes with suggestions for still more programmatic research on the questions of how children learn from, and how they come to understand smart technology—computers, smartphones, and especially humanoid robots.

Face-to-face communication is highly interactive. Even when only one person speaks at the time, other participants exchange information continuously amongst themselves and with the speaker through gesture, gaze, posture and facial expressions. This chapter argues that it is possible to significantly improve state-of-the art recognition techniques by exploiting regularities in how people communicate. For example, listeners are far more likely to nod or shake if the speaker has just asked them a question, and incorporating such dialogue context can improve recognition performance during human-robot interaction. The chapter introduces the idea of encoding dictionary, a technique for contextual feature representation inspired by the influence speaker context has on the listener feedback. Automatic selection of relevant contextual features is performed by looking at individual and joint influences of context. The final contextual integration is done using a discriminative sequential model. The chapter shows the importance of context in affective behavior understanding on two different domains: interaction with a robot and human dyadic interaction.

This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.

In the future, artificial agents are likely to make life-and-death decisions about humans. Ordinary people are the likely arbiters of whether these decisions are morally acceptable. We summarize research on how ordinary people evaluate artificial (compared to human) agents that make life-and-death decisions. The results suggest that many people are inclined to morally evaluate artificial agents’ decisions, and when asked how the artificial and human agents should decide, they impose the same norms on them. However, when confronted with how the agents did in fact decide, people judge the artificial agents’ decisions differently from those of humans. This difference is best explained by justifications people grant the human agents (imagining their experience of the decision situation) but do not grant the artificial agent (whose experience they cannot imagine). If people fail to infer the decision processes and justifications of artificial agents, these agents will have to explicitly communicate such justifications to people, so they can understand and accept their decisions.

This chapter takes the view that emotions of living machines can be seen from the perspective of self-regulation and appraisal. We will first look at the pragmatic needs to endow machines with emotions and subsequently describe some of the historical background of the science of emotions and its different interpretations and links to affective neuroscience. Subsequently, we argue that emotions can be cast in terms of self-regulation where they provide for a descriptor of the state of the homeostatic processes that maintain the relationship between the agent and its internal and external environment. We augment the notion of homeostasis with that of allostasis which signifies a change from stability through a fixed equilibrium to stability through continuous change. The chapter shows how this view can be used to create complex living machines where emotions are anchored in the need fulfillment of the agent, in this case considering both utilitarian and epistemic needs.

Robotics technologies are already impacting the ethical and moral landscape we live in. This chapter looks at the need to develop machines with ethical and moral practical wisdom, a capability we will call artificial phronesis. Phronesis is a skill that most adult humans have to some level, but as of yet no machine has it. It is the ability to think creatively, artfully, and effectively in solving novel ethical and moral problems as they arise. Many of the robots of our imagination display this skill, and this shows that there is a desire to build such machines. This chapter defines the meaning of this new term and the boundaries of which kinds of systems it might apply to.