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At CES 2016, SRI International and Yamaha Motor Co.,Ltd (Yamaha Motor) announce their collaboration toward the development of MOTOBOT, the first autonomous motorcycle-riding humanoid robot. MOTOBOT is designed to ride like a person in order to aid development of future motorcycles, improve motorcycle safety, and push the limits of what is possible in design and performance. The end performance goal of MOTOBOT is to be able to beat MotoGP™world champion rider Valentino Rossi's lap times around a racetrack.
Amish Parashar, director of Strategic Business Development at Yamaha Motor Ventures & Laboratory Silicon Valley Inc., and Thomas P. Low, associate director of Robotics at SRI International will describe MOTOBOT's joint development by Yamaha Motor and SRI during a CES 2016 Robotics Conference presentation on January 8, 2016 at 11:30am.
Yamaha Motor launched the MOTOBOT project to pioneer a new approach to vehicle development using a humanoid robot capable of operating the motorcycle with the goal of exceeding a human – with little or no modifications made to the vehicle.
To make the MOTOBOT project a rapid reality, Yamaha Motor's robotics and motorcycle engineers are collaborating with SRI International and its world-renowned robotics program. For more than 40 years, SRI has been at the forefront of robotics R&D, from applied research to the design of advanced prototypes and product development. MOTOBOT fuses Yamaha Motor's technology gained from developing motorcycles and industrial robots with SRI's expertise in developing humanoid robots that operate far more efficiently than current humanoid robotic platforms.
"Developing a humanoid vehicle rider is an ambitious effort, which is why we decided to work with SRI International, one of Silicon Valley's great R&D powerhouses. This is a strong collaboration as we bring some experience in motorcycle racing and manufacturing robotics and SRI is making contributions to the field of robotics with breakthrough components and systems," said Amish Parashar. "This project will be able to push several boundaries: visualizing data about human motorcycle operation, further quantifying the relationship between rider input and machine behavior, and then using the resulting know-how to build even better vehicles."