Posts on Jul 2016

PFN recognized as one of most innovative and exciting Japanese startups at 2016 US-Japan Innovation Awards

Toru Nishikawa, CEO, receives “Innovation Showcase” award at the symposium

Preferred Networks recognized as one of most innovative and exciting Japanese startups at 2016 US-Japan Innovation Awards

Palo Alto, CA — July 22nd, 2016 — Preferred Networks, Inc. (PFN) was selected one of the five “Innovation Showcase” companies at 2016 US-Japan Innovation Awards Symposium held in the Stanford University Arrillaga Alumni Center.

US-Japan Innovation Awards is operated by the Japan Society of Northern California in collaboration with the Stanford University US–Asia Technology Management Center. The “Innovation Showcase” is given to most exciting Japanese startup companies that have potential to disruput or transformational globally and to be known to the Sillicon Valley.

 

About Preferred Networks

Preferred Networks Inc. (PFN) is a Tokyo-based startup focusing on applications of latest artificial intelligence technologies to emerging problems in the Internet of Things (IoT). PFN’s vision is the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are intelligent. PFN develops software related to deep learning and IoT, and provides Deep Intelligence in-Motion solutions. PFN collaborates with many world-leading companies in industries, such as FANUC for intelligent robots and Toyota motors for autonomous driving. For more information please visit: www.preferred-networks.jp/en

PFN achieves Second Place at “Pick Task” of Amazon Picking Challenge

 
 
A team of Preferred Networks researchers and engineers participated in the Amazon Picking Challenge from June 29 to July 3, 2016, at Leipzig, Germany, and achieved 2nd place (score tie with 1st place) in the “pick task” and 4th place in the “stow task”.

The Amazon Picking Challenge is a competition with the objective to build a robot that can automate typical warehouse tasks without human intervention. In the “pick task” the objective is to take 12 specified items out of a shelf that also contains around 46 other items and put them into a box. In the “stow task” the objective is to take all 12 items from a box and put them into a shelf that already contains around 34 other items. In these tasks, the robot must operate without any human control. The Challenge combines object recognition, pose recognition, grasp planning, and motion planning.

The PFN Team used state-of-the-art Deep Learning algorithms and Chainer, a Python-based Open Source Deep Learning framework, on input data obtained from image and 3D location sensors for object detection and localization of the best approach position for each object. On the hardware side, the team used two FANUC robot arms of type M-10iA, equipped with self-built specialized end effectors to grab items of various characteristics reliably, then built the robot motion control from scratch.

Expertise in these technologies enabled Preferred Networks to build a robot that can compete with the world’s leading robotics research groups and companies in only three months’ time and is an important milestone on the way to revolutionize the industrial IoT and robotics.

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