Reaching development through visuo-proprioceptive-tactile integration on a humanoid robot - A deep learning approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336243" target="_blank" >RIV/68407700:21230/19:00336243 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/DEVLRN.2019.8850681" target="_blank" >http://dx.doi.org/10.1109/DEVLRN.2019.8850681</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/DEVLRN.2019.8850681" target="_blank" >10.1109/DEVLRN.2019.8850681</a>
Alternative languages
Result language
angličtina
Original language name
Reaching development through visuo-proprioceptive-tactile integration on a humanoid robot - A deep learning approach
Original language description
The development of reaching in infants has been studied for nearly nine decades. Originally, it was thought that early reaching is visually guided, but more recent evidence is suggestive of 'visually elicited' reaching, i.e. infant is gazing at the object rather than its hand during the reaching movement. The importance of haptic feedback has also been emphasized. Inspired by these findings, in this work we use the simulated iCub humanoid robot to construct a model of reaching development. The robot is presented with different objects, gazes at them, and performs motor babbling with one of its arms. Successful contacts with the object are detected through tactile sensors on hand and forearm. Such events serve as the training set, constituted by images from the robot's two eyes, head joints, tactile activation, and arm joints. A deep neural network is trained with images and head joints as inputs and arm configuration and touch as output. After learning, the network can successfully infer arm configurations that would result in a successful reach, together with prediction of tactile activation (i.e. which body part would make contact). Our main contribution is twofold: (i) our pipeline is end-to-end from stereo images and head joints (6 DoF) to armtorso configurations (10 DoF) and tactile activations, without any preprocessing, explicit coordinate transformations etc.; (ii) unique to this approach, reaches with multiple effectors corresponding to different regions of the sensitive skin are possible.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
50103 - Cognitive sciences
Result continuities
Project
<a href="/en/project/GJ17-15697Y" target="_blank" >GJ17-15697Y: Robot self-calibration and safe physical human-robot interaction inspired by body representations in primate brains</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)
ISBN
978-1-5386-8128-2
ISSN
2161-9484
e-ISSN
2161-9484
Number of pages
8
Pages from-to
163-170
Publisher name
IEEE
Place of publication
Anchorage, Alaska
Event location
Oslo
Event date
Aug 19, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—