Invited speakers

Prof. Tetsuya Ogata, Waseda University

Title: Recurrent Neural Models for Translation between Robot Actions and Language

Abstract:
There are the multiple serious difficulties for bidirectional translation between robot actions and languages, such as the segmentation of the continuous sensory-motor flows, the ambiguity and incompleteness of the sentences, the many to many mapping between actions and sentences,  and so on. In this talk, I will introduce the series of the recurrent neural models for robot's action-language learning with parametric bias and/or sequence-to-sequence manners etc. which we have proposed in these ten years.


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Prof. Daichi Mochihashi, Institute of Statistical Mathematics

Title: Inducing Motions from Movements

Abstract:
Recognizing motions, such as running, kicking, holding, or eating, from movements is a fundamental ability of human that resembles learning "words" from a sequence of characters in language. It forms a basis for further semantic processing, as well as important from a developmental point of view. In this talk, I present our approaches to this problem by extending the methods for word recognition. Employing a semi-Markov model whose emissions are Gaussian processes, we show it is able to recognize motions from robot movements in an unsupervised fasion. As to the number of latent motions, we show that a method based on hierarchical Dirichlet processes can find the proper number of motions. Finally, if time permits, I will discuss how to integrate further advanced methods in natural language processing into robotics research to enable higher levels of recognition.


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Dr. Andrzej Pronobis, University of Washington

Title: TBA

Abstract:



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Dr. Kuniyuki Takahashi, Preferred Networks

Title: Real-World Objects Interaction with Unconstrained Spoken Language Instructions

Abstract:
Comprehension of spoken natural language is an essential skill for robots to communicate with humans effectively. However, handling unconstrained spoken instructions is challenging due to complex structures and the wide variety of expressions used in spoken language, and inherent ambiguity of human instructions. For these challenges, I will introduce related research with state of art deep learning and our new research results.
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