Toyota Research Institute “deeply satisfied” with results of $100M collaborative research program with US universities
Toyota Research Institute (TRI) announced the latest results of its collaborative research program with US academic institutions. This initiative has funded $100M of research and generated more than 1,250 paper submissions since its inception in 2016, making it one of the largest collaborative research programs by an automotive company in the world.
The program expanded during 2022 to include 21 university partners and 61 projects focused on energy and materials, human-centered artificial intelligence, human interactive driving, machine learning, and robotics.
We are deeply satisfied with the results of our partnerships with this outstanding group of institutions and researchers. We believe that collaboration is the key to tackling society’s biggest challenges and are confident that this program will continue to achieve new breakthroughs.—
Each project consists of a TRI researcher working with a university team in a close collaboration bridging academia and industry.
The last year saw three papers win awards at the Robotics: Science and Systems Conference (RSS). Some project highlights from the research program include:
Advanced Robotic Capabilities. The Columbia Artificial Intelligence and Robotics (CAIR) Lab led by Computer Science Assistant Professor Shuran Song partnered with TRI to develop robotic capabilities that can handle deformable, non-rigid objects that can fold, bend, and change shape. These advancements were successfully tested by having robots fold laundry and manipulate flexible bags. Her team won a Best Paper Award at RSS 2022 for developing an algorithm called the Iterative Residual Policy (IRP), a general learning framework for repeatable tasks with complex dynamics.
Fuel Cell Catalyst Durability. Both hydrogen fuel cell vehicles and hydrogen electrolyzers require catalyst materials that utilize expensive and rare elements such as platinum and iridium. A long-standing challenge is to find replacement materials that use more abundant elements. However, all proposed alternatives don’t last long in an operating environment before they dissolve. The laboratories led by Professor Thomas Jaramillo at Stanford University and Associate Scientist Michaela Burke Stevens at the Stanford Linear Accelerator Center (SLAC) are working with TRI researchers to create data-driven theories of catalyst durability by using a novel experimental technique that enables real-time measurement of even the smallest amounts of dissolving materials.
Computational Governor. Professor Ilya Kolmanovsky’s research team at the University of Michigan developed a governor architecture that could be used to speed up the execution time of a model predictive control (MPC) control system solution. TRI researchers collaborated on and took inspiration from this project to create a solution for Toyota Motors that solves a vehicle controller problem where sudden changes to setpoints would cause unresponsiveness.
TRI plans to continue the program into 2024 with new additional high-risk, high-reward projects to accelerate the development of key technologies for Toyota.