Frontiers of Embodied Artificial Intelligence: The (r-)evolution of the embodied approach in AI

Dear Colleagues,

It’s been nearly three decades since the notion of “embodiment” has been adopted in Artificial Intelligence (AI) to characterize a (number of) research approach(es) divergent from the classic – “Computationalist” – one. The main novelty emphasized by this notion is a positive focalization on the role(s) played by the biological body in cognitive processes, which discards the traditional assumption that identifies artificial models of natural cognitive processes with purely “software models” – programs for computers reproducing cognitive performances observed in living systems, and primarily in humans. The driving idea of “Embodied AI” is that, in order to successfully explore natural cognitive processes, AI practitioners have to build and study “complete” or “embodied agents”: physically realized machines whose structure and functioning are based on biologically informed theses on adaptation and cognition. In other words, not programs, nor virtual agents, but biological-like robots: “embodied” and “situated” artificial systems that, like biological systems, provide themselves information about their environment by interacting with it, and, in this sense, learn about their environment through their interactive bodies – something that programs, or computers, cannot do.

Since the early 1990s, an increasing number of sub-divisions of Embodied AI have emerged in the attempt of modeling in robots all range of natural cognitive processes, human ones included. To this end, they have initiated an extremely effective exploration of adaptive bodily and neural mechanisms based on physiological, neuro-scientific and ethological research, which have produced more an more adaptable and autonomous biological-like robots. Applicative success has multiplied the novel domains of implementation of the embodied approach (e.g., epigenetic robotics, evolutionary robotics, developmental robotics, affective developmental robotics, among others), and made of Embodied AI the current mainstream of AI. It’s been since the late 1990s that literature empahizes how the establishment and consolidation of this approach, even if it did not implied the extinction of the classic one, produced a significant metamorphosis in AI research, which some of the proponents of Embodied AI tend to consider as a “revolution” – a “paradigmatic shift” in a Kuhnian sense.

Three decades after the emergence of Embodied AI, this Special Issue intends to explore the frontiers of the (r-)evolution it triggered in AI research. The goal is two-fold. Firstly, the Special Issue aims at mapping the most significant advancements of the avant-garde research in Embodied AI – from Enactive AI to SB-AI, from Strong Social Robotics to Artificial Empathy, from Android Science to Synthetic Ethics – and the related technical, theoretical, epistemological, and methodological challenges that they impose to AI research. Secondly, on the basis of this inquiry on front line research in Embodied AI, this Special Issue intends to address one of the most critical problems discussed by current related debate: defining the borders of Embodied AI, that is, the frontiers that distinguish its approach(es) from Classic AI. The overall purpose is to provide a perspective on contempotrary frontier trends in Embodied AI from which it is possible not only to evaluate the depth of the metamorphosis that it generated in AI, but also to identify the obstacles that currently impede Embodied AI to fully establish itself as a genuine alternative to Classic AI.

Prof. Dr. Luisa Damiano
Guest Editor

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