Posts on May 2018

Preferred Networks received the Best Paper Award on Human-Robot Interaction in ICRA 2018.

ICRA(International Conference on Robotics and Automation), one of the top conferences in Robotics organized by Institute of Electrical and Electronics Engineers (IEEE), was held in Brisbane, Australia from May 21-25, 2018. In this conference, our paper “Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions” was awarded the Best Paper Award on Human-Robot Interaction (HRI).

At the award ceremony (from left, Sosuke Kobayashi, Jun Hatori, skipping one person, Kuniyuki Takahashi, and Wilson Ko)

After the ceremony (from left, Sosuke Kobayashi, Jun Hatori, Kuniyuki Takahashi, and Wilson Ko)

 

At PFN, we are applying the latest Image processing and natural language processing technologies as a means of communication between humans and robots. Our latest work has succeeded in building an interactive system in which you can use unconstrained spoken language instructions to operate a common object picking task.

PFN will continue to research and develop the cutting-edge technology and promote its application to the industry.

 

The details of the PFN’s paper “Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions” and video are available on the following website.

https://pfnet.github.io/interactive-robot/

Chainer awarded the Open Source Data Science Project Award Winner at the ODSC East 2018

The Open Source Data Science Project award is given in recognition for the significant contribution to the field of data science. Winners in previous years were the Pandas Project and scikit-learn.

Chainer, an open source deep learning framework, won the award this year, in the recognition of its dynamic and flexible neural network definition by “define-by-run”.

 

 

Chainer is evaluated for the award as follows:
Chainer strives to “bridge the gap between algorithms and deep learning implementations” in its flexible and intuitive Python-based framework for neural networks. Chainer was the first framework to provide the “define-by-run” neural network definition which allows for dynamic changes in the network. Since flexibility is a significant part of the foundations of Chainer, the framework allows for customization that similar platforms do not so easily provide and supports computations on either CPUs or GPUs.

https://opendatascience.com/odsc-east-2018-open-source-data-science-project-award-winner-the-chainer-framework/

 

About the Open Data Science Conference (ODSC)

ODSC is a conference for people to connect with the data science community and contribute to the open source applications they use every day. Its goal is to bring together the global data science community to help foster the exchange of innovative ideas and encourage the growth of open source software.

 

 

About the Chainer Open Source Deep Learning Framework

Chainer is a Python-based deep learning framework developed mainly by PFN, which has unique features and powerful performance that allow for designing complex neural networks easily and intuitively, thanks to its “Define-by-Run” approach. Since it was open-sourced in June 2015, as one of the most popular frameworks, Chainer has attracted not only the academic community but also many industrial users who need a flexible framework to harness the power of deep learning in their research and real-world applications.
Chainer incorporates the results of the latest deep learning research. With additional packages such as ChainerMN (distributed learning), ChainerRL (reinforcement learning), ChainerCV (computer vision) and through the support of Chainer development partner companies, PFN aims to promote the most advanced research and development activities of researchers and practitioners in each field. (http://chainer.org/)