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Cognitive Neuro-robotics
A Deep Learning Approach for Seamless Integration of Cognitive Skills for Humanoid Robots, 2014 - Present
Research Project supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP), Korea

Abstract
The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN). The proposed model is built on coupling of a dynamic vision network, a motor generation network, and a higher level network allocated on top of these two. The simulation experiments using the iCub simulator were conducted for cognitive tasks including visual object manipulation responding to human gestures. The results showed that ¡°synergetic¡± coordination can be developed via iterative learning through the whole network when spatio-temporal hierarchy and temporal one can be self-organized in the visual pathway and in the motor pathway, respectively, such that the higher level can manipulate them with abstraction.

Additional information
  • Hwang, J., Jung, M., Kim, J. & Tani, J. (2016). A Deep Learning Approach for Seamless Integration of Cognitive Skills for Humanoid Robots. In 2016 The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Cergy-Pontoise, France. [PDF]
  • Hwang, J., Jung, M., Madapana, N., Kim, J., Choi, M., & Tani, J. (2015). Achieving "synergy" in cognitive behavior of humanoids via deep learning of dynamic visuo-motor-attentional coordination. In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 817-824. IEEE. [Link]



 
Human-Robot Interaction
Development of a self-improving bidirectional sustainable HRI technology for 95% of successful responses with understanding user¡¯s complex emotion and transactional intent through continuous interactions, 2013 - 2014
Overview
Research Project with the Ministry of Trade, Industry and Energy (MOTIE, Korea), Korea

- Creative Robot Actions Generation from the Autonomous Dynamic Network (RNNPB)