In 1933, Toyota recognized that the world was changing. While our company was making fabric looms, automobiles were defining the future. The company did an incredibly brave thing—it used capital from its loom business to start in a new business: automotive. TRI reflects a similar understanding. Toyota has traditionally been a hardware company first and foremost because in the past the most important technology for enhancing human mobility was hardware. Times have changed, and software and data are now essential components of Toyota’s future mobility strategy.

Furthermore, the need for machines to assist in mobility has broadened from outdoors to indoors, fueled by our aging society and the remarkable progress in electronics that has expanded the realm of what is possible. This is why TRI is working to transform mobility from outdoors to indoors. Home robots may be even more personally prized in our future than cars have been in our past.

TRI’s goal is to the bridge the gap between Research and Development—to make a real difference in what products Toyota produces in the future. It is entirely possible that robots will become for today’s Toyota what the car industry was when Toyota made looms.

Dr. Pratt earned his doctorate from the Electrical Engineering and Computer Science department at MIT, and taught at MIT and Olin College as Associate Professor and Professor, respectively. He established three startups between 1983 and 2005, and helped found Olin College. As a DARPA Program Manager, he led projects in Neuromorphic Systems and Robotics, including the DARPA Robotics Challenge, from 2010 to 2015.

On the road to that larger vision, however, are a number of immediate tasks, Dr. Pratt observed, among them setting a radically higher bar for the reliable performance of autonomous driving.

Although the industry has made great strides over the last five years, we are a long way from the finish line of fully automated cars. Tomorrow, when you look across the CES displays, at what is currently being tested and developed, you will find that these systems can only handle certain speed ranges, certain weather conditions, certain street complexity, or certain traffic. Despite the progress you will see, most of what has been collectively accomplished has been relatively easy because most driving is easy.

Where we need autonomy to help us, is when the driving is difficult. And it’s this hard part that we intend to address. Up to now, our industry has measured on-road reliability of autonomous vehicles, in the millions of miles, which is impressive. But to achieve full autonomy we actually need reliability that is a million times better.

The roughly 100 million Toyota cars and trucks in service at any given time in the world travel a total of about 1 trillion miles per year. Even if a very small percentage of that driving is difficult, that percentage times a trillion equals many miles of difficult driving that we must address. Society tolerates a lot of human error. But we expect machines to be much better. We expect them to be ever-ready and nearly perfect. Thus, the technologies we develop have to work not only at the million-mile scale, but at the trillion-mile scale.

—Dr. Pratt

To address this and other issues, TRI has four initial mandates:

1. Enhance automobiles with the ultimate goal of creating a car that is incapable of causing a crash, regardless of the skill or condition of the driver.

2. Increase access to cars to those who otherwise cannot drive, including people with special needs and seniors.

3. Help to translate Toyota’s expertise in creating products for outdoor mobility into products for indoor mobility. In other words, Toyota’s goal is to move people across the room, across town, and across the country.

4. To accelerate scientific discovery by applying techniques from artificial intelligence and machine learning particularly in the area of materials science. Toyota aims to utilize computation and machine learning to accelerate scientific discovery in this area to lower the cost and improve the performance of future mobility systems.

To those ends, TRI has launched 30 initial projects with its partners at Stanford and MIT.

Dr. Pratt also introduced the initial team of research directors:

• Eric Krotkov, Former DARPA Program Manager – Chief Operating Officer

• Larry Jackel, Former Bell Labs Department Head and DARPA Program Manager – Machine Learning

• James Kuffner, CMU Professor and former head of Google Robotics – Cloud Computing

• John Leonard, Samuel C. Collins Professor of Mechanical and Ocean Engineering, MIT – Autonomous Driving

• Hiroshi Okajima, Project General Manager, R&D Management Division, Toyota Motor Corporation – Executive Liaison Officer

• Brian Storey, Professor of Mechanical Engineering, Olin College of Engineering – Accelerating Scientific Discovery

• Russ Tedrake, Associate Professor in the Department of Electrical Engineering and Computer Science, MIT – Simulation and Control

Professors Tedrake, Leonard and Storey will work part time with TRI and continue in their university roles.

An Advisory Board will be made up of a range of outside experts including:

• John Roos, former CEO of Wilson Sonsini and former US Ambassador to Japan. Currently, General Partner at Geodesic Capital, a late stage venture capital firm and Senior Advisor at Centerview Partners, a preeminent mergers and acquisitions advisory firm – Chairman

• Rodney Brooks, former director of the MIT Computer Science and AI Lab, founder of iRobot and Founder, Chairman and CTO of Rethink Robotics – Deputy Chairman

• Marc Benioff, CEO of Salesforce.com

• Richard Danzig, former Secretary of the Navy

• Bran Ferren, former President of R & D at Walt Disney Imagineering and Chief Creative Officer of Applied Minds

• Noboru Kikuchi (planned), Emeritus University of Michigan Professor, head of Toyota Central Research and Development Lab and the Toyota Research Institute of North America

• Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory (SAIL)

• Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory