The Future of Intelligent Automated Logistics

James Kuffner

Chief Technology Officer at Symbotic

Location: Gates, 403 Fujitsu Conference Room (link)

Abstract

How do you orchestrate a fleet of thousands of robots in real-time to manage the movement of millions of cartons through a warehouse per day? In this session, James Kuffner, Chief Technology Officer of Symbotic – a pioneering leader in robotics – will discuss his transition to Symbotic, a company delivering advanced AI-powered logistics robot fleets to the global supply chain. The recent rapid progress in machine learning, computer vision and perception, as well as increased computing power have enabled a new generation of intelligent robots that can operate reliably in semi-structured environments like warehouses. Innovative new algorithms for multi-robot motion and task planning will scale the technology even further, creating exciting new opportunities for the future of intelligent automated logistics.

Bio: 

James Kuffner is the Chief Technology Officer at Symbotic. In that role, James is responsible for building and advancing all Symbotic’s technologies and solutions. James has served as Symbotic's Chief Technology Officer since January 2025 and brings more than 30 years of experience in robotics within both academic and industry research. Prior to joining Symbotic, James was at Toyota Motor Corporation, where he held several leadership roles during his long tenure. Most recently, he was a Senior Fellow in charge of the company’s Software Development Center, after serving as Toyota’s Chief Digital Officer and Member of the Board. Prior to Toyota, James was a leader at Google for a variety of Engineering functions, including as the head of Google’s Robotics division, where he helped develop Google’s self-driving car and managed research projects. Before Google, James was an Associate Professor at Carnegie Mellon University’s Robotics Institute, leading research and teaching both computer science and robotics. As the author of 125 publications and 40 patents in 3D graphics, robotics and autonomous vehicles, James is known as the co-inventor of the Rapidly Exploring Random Tree (RRT) algorithm for robot motion planning. James has a Ph.D., M.S. and B.S. in computer science from Stanford University.