IROS2020 Tutorial sponsored by the robotics society of Japan (RSJ)
Deep Probabilistic Generative Models for Robotics
Message from President of RSJ
Welcome to Tutorial on Deep Probabilistic Generative Models for Robotics!
It is my great pleasure to welcome you all to this tutorial presented by the Robotics Society of Japan (RSJ). RSJ provides a tutorial as a contribution to IROS every year. The topics of the tutorial are selected on the basis of their significance, novelty, and potential for future research. This year we present a tutorial on deep probabilistic generative models (DPGMs), which has received a great deal of attention in the field of machine learning, and its application to intelligent robots is very promising. This tutorial consists of theories and applications of the DPGMs for robotics. Furthermore, two useful software platforms are introduced to put the theory into practice. I hope that you will find this on-demand tutorial stimulating and a source of inspiration for future research.
President, The Robotics Society of Japan
Deep probabilistic generative models, machine learning, cognitive functionalities, robotic control, Pixyz, and Serket
The goal of this tutorial is to bring together students and researchers from robotics and machine learning to share knowledge about deep probabilistic generative models to develop future cognitive architectures for robots. In IROS 2019, we organized the workshop on deep probabilistic generative models for cognitive architecture in robotics aiming at examining the challenges and opportunities emerging from the interdisciplinary research field covering machine learning, cognitive science, and robotics. In the workshop, we investigated how we could create a cognitive architecture for a robot using deep and probabilistic generative models. We shared knowledge about the state-of-the-art machine learning methods that contribute to model 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 models.
Based on this experience, a tutorial on the usage of “pixyz” as well as the basic theories of deep generative models will be provided in order to advance the research utilizing the discussions last year. Pixyz is a high-level deep generative modeling library, based on PyTorch. It is developed with a focus on enabling easy implementation of various deep generative models (https://github.com/masasu/pixyz). Knowing the usage of this tool will be a big advantage for implementing the deep generative models.
We will also give some comprehensive examples of applications of probabilistic generative models for robotics followed by the tutorial on “Serket.” Serket is another useful tools for integrating different types of probabilistic generative models, which is useful for developing a large scale cognitive model for robots (http://serket.naka-lab.org/).
We believe the topic of this tutorial is timely and necessary for the IEEE-RAS community.
The targeted audience includes:
graduate students and robotics researchers who are interested in machine learning and deep learning (especially probabilistic generative models),
graduate students and robotics researchers who are interested in building integrative cognitive architecture using deep generative models
graduate students and robotics researchers who are interested in the integration of high-level and low-level cognitive capabilities in a robot,
graduate students and robotics researchers who are interested in hierarchical Bayesian modeling of cognitive capabilities,
graduate students, and machine learning and artificial intelligence researchers who are interested in the application of deep probabilistic generative models and multimodal machine learning to robotics,
people in industry that are trying to develop real-world application using robotic and machine learning technologies.
University of Tokyo
The University of Electro-Communications
How to join
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