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Technology is giving rise to artificial erotic agents, which we call erobots ( erôs + bot). Erobots, such as virtual or augmented partners, erotic chatbots, and sex robots, increasingly expose humans to the possibility of intimacy and sexuality with artificial agents. Their advent has sparked academic and public debates: some denounce their risks (e.g., promotion of harmful sociosexual norms), while others defend their potential benefits (e.g., health, education, and research applications). Yet, the scientific study of human–machine erotic interaction is limited; no comprehensive theoretical models have been proposed and the empirical literature remains scarce. The current research programs investigating erotic technologies tend to focus on the risks and benefits of erobots, rather than providing solutions to resolve the former and enhance the latter. Moreover, we feel that these programs underestimate how humans and machines unpredictably interact and co-evolve, as well as the influence of sociocultural processes on technological development and meaning attribution. To comprehensively explore human–machine erotic interaction and co-evolution, we argue that we need a new unified transdisciplinary field of research—grounded in sexuality and technology positive frameworks—focusing on human-erobot interaction and co-evolution as well as guiding the development of beneficial erotic machines. We call this field Erobotics . As a first contribution to this new discipline, this article defines Erobotics and its related concepts; proposes a model of human-erobot interaction and co-evolution; and suggests a path to design beneficial erotic machines that could mitigate risks and enhance human well-being.

Face recognition is a highly specialized capability that has implicit and explicit memory components. Studies show that learning tasks with facial components are dependent on rapid eye movement and non-rapid eye movement sleep features, including rapid eye movement sleep density and fast sleep spindles. This study aimed to investigate the relationship between sleep-dependent consolidation of memory for faces and partial rapid eye movement sleep deprivation, rapid eye movement density, and fast and slow non-rapid eye movement sleep spindles. Fourteen healthy participants spent 1 night each in the laboratory. Prior to bed they completed a virtual reality task in which they interacted with computer-generated characters. Half of the participants (REMD group) underwent a partial rapid eye movement sleep deprivation protocol and half (CTL group) had a normal amount of rapid eye movement sleep. Upon awakening, they completed a face recognition task that contained a mixture of previously encountered faces from the task and new faces. Rapid eye movement density and fast and slow sleep spindles were detected using in-house software. The REMD group performed worse than the CTL group on the face recognition task; however, rapid eye movement duration and rapid eye movement density were not related to task performance. Fast and slow sleep spindles showed differential relationships to task performance, with fast spindles being positively and slow spindles negatively correlated with face recognition. The results support the notion that rapid eye movement and non-rapid eye movement sleep characteristics play complementary roles in face memory consolidation. This study also raises the possibility that fast and slow spindles contribute in opposite ways to sleep-dependent memory consolidation.