A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362310" target="_blank" >RIV/68407700:21230/22:00362310 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ICDL53763.2022.9962195" target="_blank" >https://doi.org/10.1109/ICDL53763.2022.9962195</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDL53763.2022.9962195" target="_blank" >10.1109/ICDL53763.2022.9962195</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot

Popis výsledku v původním jazyce

Postnatal development in infants involves building the body schema based on integrating information from different modalities. An early phase of this complex process involves coupling proprioceptive inputs with tactile information during self-touch enabled by motor babbling. Such functionality is also desirable in humanoid robots that can serve as embodied instantiation of cognitive learning. We describe a simple connectionist model composed of neural networks that learns the proprioceptive-tactile representations on a simulated iCub humanoid robot. Input signals from both modalities – joint angles and touch stimuli on both upper limbs – are first self-organized in neural maps and then connected using a universal bidirectional associative network (UBAL). The model demonstrates the ability to predict touch and its location from proprioceptive information with relatively high accuracy. We also discuss limitations of the model and the ideas for future work.

Název v anglickém jazyce

A connectionist model of associating proprioceptive and tactile modalities in a humanoid robot

Popis výsledku anglicky

Postnatal development in infants involves building the body schema based on integrating information from different modalities. An early phase of this complex process involves coupling proprioceptive inputs with tactile information during self-touch enabled by motor babbling. Such functionality is also desirable in humanoid robots that can serve as embodied instantiation of cognitive learning. We describe a simple connectionist model composed of neural networks that learns the proprioceptive-tactile representations on a simulated iCub humanoid robot. Input signals from both modalities – joint angles and touch stimuli on both upper limbs – are first self-organized in neural maps and then connected using a universal bidirectional associative network (UBAL). The model demonstrates the ability to predict touch and its location from proprioceptive information with relatively high accuracy. We also discuss limitations of the model and the ideas for future work.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2022 IEEE International Conference on Development and Learning (ICDL)

ISBN

978-1-6654-1311-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

336-342

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

London

Datum konání akce

12. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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