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    Body models in humans and robots

    Hoffmann, M. and Longo, Matthew (2022) Body models in humans and robots. In: Alsmith, A.J.T. and Longo, Matthew (eds.) Routledge Handbook of Bodily Awareness. Routledge. ISBN 9780367337315. (In Press)

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    Humans excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent—yet, as is so often the case, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent—human or robot—has developed. In the biological realm, evidence has been accumulated by diverse disciplines giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables control of the machine. In this chapter, we compare the character of body representations in biology with their robotic counterparts and relate that to the differences in performance that we observe. In some sense, robots have a lot in common with Ian Waterman—“the man who lost his body”—in that they rely on an explicit, veridical body model (body image taken to the extreme) and lack any implicit, multimodal representation (like the body schema) of their bodies. The core of this work is a detailed look at the somatoperceptual processing “pipeline” from inputs (tactile and proprioceptive afference, efferent commands), over “body representations” (superficial schema, postural schema, model of body size and shape), to perceptual processes like spatial localization of touch. A direct comparison with solutions to the same task in robots allows us to make important steps in converting this conceptual schematics into a computational model. As an additional aspect, we briefly look at the question of why robots do not experience body illusions. Finally, we discuss how robots can inform the biological sciences dealing with body representations and which of the features of the “body in the brain” should be transferred to robots, giving rise to more adaptive and resilient, self-calibrating machines.


    Item Type: Book Section
    Additional Information: This is an Accepted Manuscript of a book chapter published by Routledge.
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Psychological Sciences
    Depositing User: Matthew Longo
    Date Deposited: 08 Jul 2022 12:57
    Last Modified: 02 Aug 2023 18:14


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