Virtual competitors vie for a different kind of athletic title | Stanford … – Stanford University News

Modeling the walk

Kidziski works in the lab of Scott Delp, a professor of bioengineering and of mechanical engineering who has spent decades studying the mechanics of the human body. As part of that work, Delp and his collaborators have collected data on the movements and muscle activity of hundreds of individuals as they walk and run.

With data like that, Delp, Kidziski and their team can build accurate models of how individual muscles and limbs move in response to signals from the brain.

But what they could not do was predict how people relearn to walk after surgery because, as it turns out, no one is quite sure how the brain controls complex processes like walking, let alone walking through the obstacle course of daily life or relearning how to walk after surgery.

Whereas weve gotten quite good at building computational models of muscles and joints and bones and how the whole system is connected how the human machine is built an open challenge is how your brain orchestrates and controls this complex dynamic system, Delp said.

Machine learning, a variety of artificial intelligence, has reached a point where it could be a useful tool for modeling of the brains movement control systems, Delp said, but for the most part its practitioners have been interested in self-driving cars, playing complex games like chess or serving up more effective online ads.

The time was right for a challenge like this, Delp said, in part because some in the machine learning community are looking for more meaningful problems to work on, and because bioengineers stand to gain from understanding more about machine learning. His labs most successful efforts to model human movement have come from efforts to represent neural control of movement, Delp said, and machine learning is likely a realistic way to think about learning to walk.

So far, 63 teams have submitted a total of 145 ideas to Kidziskis competition, which is one of five similar contests created for the 2017 Neural Information Processing Systems conference. Kidziski supplies each team with computer models of the human body and the world that body must navigate, including stairs, slippery surfaces and more. In addition to external challenges, teams also face internal ones, such as weak or unreliable muscles. Each team is judged based on how far its simulated human makes it through those obstacles in a fixed amount of time.

Kidziski and Delp hope that more teams will join their competition, and with about two months remaining, they hope that at least a few teams will overcome all the various virtual obstacles thrown in their way. (No one has done so yet the top teams have for the most part conquered walking, but none has attempted the more athletic maneuvers.) The challenge, Kidziski said, is very computationally expensive.

In the long run, Kidziski said he hopes the work may benefit more than just kids with cerebral palsy. For example, it may help others design better-calibrated devices to assist with walking or carrying loads, and similar ideas could be used to find better baseball pitches or sprinting techniques.

But, Kidziski said, he and his collaborators have already created something important: a new way of solving problems in biomechanics that looks to virtual crowds for solutions.

Delp is the James H. Clark Professor in the School of Engineering and a member of Stanford Bio-X and the Stanford Neurosciences Institute. Graduate student Carmichael Ong, postdoctoral fellow Jason Fries, Mobilize Center Director of Data Science Jennifer Hicks and Mohanty Sharada coordinated the project. Sergey Levine, Marcel Salath and Delp serve as advisors

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