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.

References:

Amazon Picking Challenge : https://www.amazonrobotics.com/#/pickingchallenge