Body models in humans and robots
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362226" target="_blank" >RIV/68407700:21230/22:00362226 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.4324/9780429321542-18" target="_blank" >https://doi.org/10.4324/9780429321542-18</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4324/9780429321542-18" target="_blank" >10.4324/9780429321542-18</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Body models in humans and robots
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Body models in humans and robots
Popis výsledku anglicky
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.
Klasifikace
Druh
C - Kapitola v odborné knize
CEP obor
—
OECD FORD obor
50103 - Cognitive sciences
Návaznosti výsledku
Projekt
<a href="/cs/project/GX20-24186X" target="_blank" >GX20-24186X: Vědomí celého povrchu těla pro bezpečnou a přirozenou interakci: od mozku ke kolaborativním robotům</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název knihy nebo sborníku
The Routledge Handbook of Bodily Awareness
ISBN
978-0-367-33731-5
Počet stran výsledku
13
Strana od-do
185-197
Počet stran knihy
570
Název nakladatele
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Místo vydání
Oxon
Kód UT WoS kapitoly
—