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Welcome to the Workshop on Deep Probabilistic Generative Models for Cognitive Architecture in Robotics

The goal of this workshop is to bring together researchers from robotics and machine learning to share knowledge about deep and probabilistic generative models to develop a future cognitive architecture for robots. The workshop also aims at examining the challenges and opportunities emerging from the interdisciplinary research field covering machine learning, cognitive science, and robotics.
Success in deep learning has enabled robots to recognize their environment, e.g., visual and speech recognition,  and to learn behaviors efficiently, e.g., reinforcement and imitation learning.  However, most of the success in deep learning is heavily depending on labeled data or hand-crafted reward functions that need to be prepared before the learning process. 

In this workshop, we will investigate how we can create a cognitive architecture for a robot using deep and probabilistic generative models. To this end, we aim to share knowledge about the state-of-the-art machine learning methods that contribute to modeling language-related capabilities in robotics, and to exchange views among cutting-edge robotics researchers with a special emphasis on the usage of deep generative models in robotics and modeling a wide range of cognitive capabilities using probabilistic generative   The workshop will include keynote presentations from established researchers in robotics, machine learning, and cognitive science. There will be a poster session highlighting contributed papers throughout the day.


News
  • (10th March 2019) The website is open.

Important Dates 
  • Submission of abstracts:      September 20, 2019
  • Notification of acceptance:   September 31, 2019
  • Workshop: November 8, 2019


Organizers
  • Takato Horii, Osaka University
  • Tadahiro Taniguchi, Ritsumeikan University
  • Tetsunari Inamura, National Institute of Informatics
  • Lorenzo Jamone, Queen Mary University of London
  • Takayuki Nagai, Osaka University
  • Yiannis Demiris, Imperial College London
Program committee members
  • Xavier Hinaut, INRIA
  • Michael Spranger, Sony Computer Science Laboratories Inc.
  • Emre Ugur, Bogazici University
  • Yoshinobu Hagiwara, Ritsumeikan University, Japan
  • Tetsuya Ogata, Waseda University