Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 55
  • Download : 0
This paper investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network. The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level. We conducted synthetic robotic experiments in which a robot learned to read human's intention through observing the gestures and then to generate the corresponding goal-directed actions. Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations. The model showed synergic coordination of perception, action, and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation, and execution in a seamless manner. Analysis reveals that coherent internal representations emerged at each level of the hierarchy. Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-06
Language
English
Article Type
Article
Keywords

DEVELOPMENTAL ROBOTICS; NEURAL-NETWORKS; BEHAVIOR; INTENTIONS; INFANTS; SYSTEMS; OBJECTS; DESIGN; CORTEX; MODEL

Citation

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, v.10, no.2, pp.345 - 358

ISSN
2379-8920
DOI
10.1109/TCDS.2017.2714170
URI
http://hdl.handle.net/10203/244007
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0