Drake: In the age of robotics and machine learning, model-based design is essential

NEW YORK CITY, Afghanistan - Sept. 3, 2021 - PRLog -- I assumed that an industry research lab like Toyota Research Institute (TRI) might make fundamental contributions to robotics that would be difficult to make in an academic lab or a startup when I joined more than five years ago. And I joined with the promise that we would use open-source software to share our greatest tools and findings with the rest of the world.

I competed in the DARPA Robotics Challenge just before joining TRI to programme a humanoid robot for near-autonomous operation in a disaster response scenario. This experience taught me the importance of software engineering and made me understand that the world has never seen genuinely mature implementations of the best ideas from control theory, machine learning, mathematical optimization, and verification applied to large-scale robots.

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The addition of scale and real-world testing raises a slew of fundamental research issues that aren't apparent in simpler prototypes. To examine uncommon failures, for example, a system that is successful 99.9% of the time when executing a relevant task is required (like loading the dishwasher for in-home robots). In academia, that is extremely difficult to attain. Another issue to consider is fleet learning.

Roboticists are prepared to take on these difficulties, and industrial research labs such as TRI can help. Deep learning's ascent has been powered not only by a few brilliant conceptual concepts, but also by prominent industrial labs' significant investments in software and hardware tools, as well as their decision to make these tools open source. Deep networks have become an increasingly significant aspect of a roboticist's toolset, yet they still only make up a small part of it.

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