Wednesday, January 16, 2019

Ambidextrous Robot Could Speed Packaging at E-Commerce Fulfillment Centers

From spoons to stuffed animals, humans learn early in life how to pick up objects that have a variety of shapes, textures and sizes.

A new machine-learning algorithm developed by engineers at UC Berkeley can teach robots to grasp and carry items with similar dexterity.

The algorithm helps “ambidextrous” robots equipped with different types of grippers — for example, a suction gripper and a parallel-jaw gripper — decide which gripper to use for any given object.

A new algorithm helps robots decide which gripper works best for picking up different objects. 
A grey robot with a suction gripper on one arm and a claw gripper on the other is poised above a container of objects.
Credit: Adriel Olmos, UC Berkeley photo

“Any single gripper cannot handle all objects,” said Jeff Mahler, a postdoctoral researcher at UC Berkeley and lead author of a new paper describing the advance, published this week in Science Robotics. “For example, a suction cup cannot create a seal on porous objects such as clothing, and parallel-jaw grippers may not be able to reach both sides of some tools and toys. ‘Ambidextrous’ robots offer greater diversity.”

The technology could be especially useful in fulfillment centers for e-commerce companies like Amazon, which rely on robots for packaging.

“When you are in a warehouse putting together packages for delivery, objects vary considerably,” said Ken Goldberg, a UC Berkeley professor with joint appointments in the Department of Electrical Engineering and Computer Sciences and the Department of Industrial Engineering and Operations Research. “We need a variety of grippers to handle a variety of objects.”

Contacts and sources:
University of California - Berkeley


Citation: Learning ambidextrous robot grasping policies.
Jeffrey Mahler, Matthew Matl, Vishal Satish, Michael Danielczuk, Bill DeRose, Stephen McKinley, Ken Goldberg. Science Robotics, Jan. 16, 2019; DOI: 10.1126/scirobotics.aau4984


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