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. When you look at what is currently being tested and developed in the field of autonomous vehicles, you will find that these systems can only handle certain speed ranges, certain weather conditions, certain street complexity, or certain traffic. 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 TRI intends to address. That means understanding difficult driving scenarios, and building AI systems that learn from, and evolve to predict, such scenarios. 

TRI will also apply AI technology to the challenge of home robotics. Here, fueled by our aging society and the remarkable progress in electronics and computer science, we see a need for machines to assist in mobility beyond the realm of what is currently possible. Home robots may become even more personally prized in our future than cars have been in our past. It is entirely possible that robots will become for today’s Toyota what the car industry was when Toyota made looms.

Finally, Toyota's quest for a new world of mobility also depends on novel materials. Robots in the home will need materials that are stronger, lighter, and less expensive than the machines on the factory floor. Electric vehicles need a new type of battery that stores energy as efficiently as gasoline, and is equally simple and fast to recharge as a gas tank. And fuel cell technology currently requires methods to store and process Hydrogen at high pressure, posing significant engineering challenges. TRI wants to apply the promise of machine learning and AI to discover, even design, new materials that will vault past these limitations.

Society tolerates a lot of human error. But we expect machines to be much better. We expect them to be bullet-proof… ever-ready… and nearly perfect. However, achieving this level of quality is difficult given the issues that exist in the current state-of-the-art in AI software. Presently, many of the most advanced AI systems use machine learning techniques where large datasets are used to train the software how to respond. A challenge with this approach is that it is hard to know what the system will do when faced with a novel input. 

It is this quest for safety, utility and reliability that drives TRI to understand the hard parts of driving, to understand how humans can benefit from robot assistants, and to understand how to apply AI to designing and creating new materials. 



Gill Pratt


Dr. Gill Pratt is the Chief Executive Officer of Toyota Research Institute (TRI), a research and development enterprise designed to bridge the gap between fundamental research and product development. Launched in 2016, TRI’s mission is to enhance the safety of automobiles, with the ultimate goal of creating a car that is incapable of causing a crash. It seeks to provide increased access to cars for those who otherwise cannot drive, including those with special needs and seniors. Furthermore, TRI looks to translate outdoor mobility technology into products for indoor mobility, and accelerate scientific discovery by applying techniques from artificial intelligence and machine learning. Dr. Pratt also serves as the Executive Technical Advisor to Toyota Motor Corporation.



Eric Krotkov


Dr. Eric Krotkov serves as the Chief Science Officer of the Toyota Research Institute (TRI), where he directs strategic research and development, and leads the company's university collaborations. Dr. Krotkov joined TRI at its founding in 2016 and served as the Chief Operating Officer.  From 2001 to 2015, Dr. Krotkov was the President of Griffin Technologies, where he consulted on robotics for DARPA and other Government agencies. Before founding Griffin, he worked in industry as an executive in a medical imaging technology start-up, in government as a program manager at DARPA, and in academia as a faculty member of the Robotics Institute at Carnegie Mellon University. Dr. Krotkov earned his Ph.D. degree in Computer and Information Science in 1987 from the University of Pennsylvania, for pioneering work in active computer vision.



James Kuffner


Dr. James Kuffner is the Chief Technology Officer at the Toyota Research Institute and an Adjunct Associate Professor at the Robotics Institute, Carnegie Mellon University.  From 2009 to 2016, Dr. Kuffner was a Research Scientist and Engineering Director at Google.  Dr. Kuffner received a Ph.D. from the Stanford University Dept. of Computer Science Robotics Laboratory in 1999, and was a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo.  He joined the faculty at Carnegie Mellon University's Robotics Institute in 2002.




Kelly Kay


Kelly Kay is the Chief Operations Officer at the Toyota Research Institute (TRI) responsible for Human Resources, IT, Legal/Compliance, Facilities and overall operations.  Prior to TRI, Kelly served as the Vice President of Business Operations at Lyft, Inc., where she built and led the teams responsible for Regulatory Compliance, Audit & Reporting, Payments & Fraud, and Airport Operations.  She also served as the Chief Operating Officer and President of YapStone, Inc., a leading electronic payments company in the real property space.




Palo Alto, CA

Located a short 10 minute bike ride or walk from our collaborators at Stanford University, TRI's Palo Alto office echos the excitement and innovation found throughout Silicon Valley. 

Ann Arbor, Mi

Opened in mid-2016, our Ann Arbor office is positioned next to the University of Michigan and close to Toyota's US engineering and technology center, offering unique technology transfer opportunities. 


Cambridge, MA

TRI's Cambridge office in the booming Kendall Square technology park area is a short walk from MIT, enabling close collaboration on a number of research projects.