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Scientists designing agile robots and efficient prosthetics can soon expect a new tool in their toolbox.  

A University of Toronto computer scientist, researchers from Massachusetts Institute of Technology (MIT) and Adobe Research have developed a set of computational methods that will help automate the design of mechanisms that propel movement. 

“The goal is to bring the physical rules of virtual reality much closer to those of actual reality,” says David I. W. Levin, an assistant professor and researcher in the lab at U of T’s department of computer science. 

Levin researches computational physics, a field which develops methods for a wide range of applications from biomechanical models of muscle to mechanical analysis of 3D printed objects.

“Computers are great at designing things, but they need to be able to predict the real-world behaviour of whatever it is they are designing,” says Levin. “That’s where computer simulation comes in.”

The researchers will present methods such as Dynamics-Aware Coarsening (DAC), which speeds-up simulations, and Boundary Balanced Impact (BBI), which models the impact behaviour of flexible objects. Together, these two methods simulate objects performing real-world flips, throws and jumps at rates 70 times faster than current state-of-the-art methods, while simultaneously improving the accuracy of such computer simulations.

Levin and lead author, MIT PhD student Desai Chen, as well as MIT Professor Wojciech Matusik and Adobe researcher Danny M. Kaufman will present this week at the world's largest conference and exhibition in computer graphics and interactive techniques. 

Prior to joining U of T, Levin was an associate research scientist at Disney Research, which takes computer graphics and animation outside of the studio, applying it to a real-world problem. 

“Dynamics is crucial for understanding how humans and animals move and for building everything from better car tires to more efficient prosthetics,” he says. “That’s what is so exciting about DAC and BBI – they give us a new, powerful, broadly applicable computational tool.”

The work was supported in part by the National Science Foundation (NSF), and Levin's research has also received funding from the Connaught New Researcher Award.