Ken Goldberg at Robotics at Stanford Seminar
Stanford Robotics and Autonomous Systems Seminar held guests throughout the semester to foster the robotics community on the Stanford campus along with connecting them with speakers from outside the community.
Ken Goldberg presented to the series on Novermber 30th in a presentation titled: "A Grand Challenge for E-Commerce: Optimizing Rate, Reliability, and Range for Robot Bin Picking and Related Projects."
From the abstract:
Consumer adoption of e-commerce is skyrocketing at Amazon, Walmart, JD.com, and Alibaba. As new super-sized warehouses are opening every month, it is proving increasingly difficult to hire enough workers to meet the pressing need to shorten fulfillment times. Thus a Holy Grail for e-commerce is robots that are capable of Universal Picking: reliably and efficiently grasping a massive (and changing) set of products of diverse shapes and sizes.
I'll describe a "new wave" in research that combines classical mechanics, stochastic, and deep learning. The First Wave of grasping research, still dominant, uses analytic methods based on screw theory and assumes exact knowledge of pose, shape, and contact mechanics. The Second Wave is empirical: purely data driven approaches which learn grasp strategies from many examples using techniques such as imitation and reinforcement learning with hyperparametric function approximation (Deep Learning). I'll present the Dexterity Network (Dex-Net), a New Wave method being developed by UC Berkeley startup Ambidextrous Laboratories hat combines analytic and empirical approaches to rapidly synthesize massive training datasets that incorporate statistical analytic models of the inherent errors arising from physics, sensing, and control. Dex-Net can be applied to almost any combination of robots, bins, shelves, 3D sensors, and gripping devices and is achieving record-breaking performance in picks per hour on novel objects.