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MIT researchers develop a much better way to optimize the control of soft robots

Darrell Etherington
An 'origami gripper' soft robot designed by MIT's CSAIL. These types of robots would benefit from this new research.

MIT researchers have developed a new way to optimize how soft robots perform specific tasks -- a huge challenge when it comes to soft robotics in particular, because robots with flexible bodies can basically move in an infinite number of ways at any given moment, so programming them to do something in the best way possible is a monumental task.

To make the whole process easier and less computationally intensive, the research team has developed a way to take what is effectively a robot that can move in infinite possible dimensions and simplify it to a representative "low-dimensional" model that can accurately be used to optimize movement, based on environmental physics and the natural ways that soft objects shaped like any individual soft robot is actually most likely to bend in a giving setting.

So far, the MIT team behind this has demonstrated it in simulation only, but in this simulated environment it has seen significant improvements in terms of both speed and accuracy of programmed movement of robots versus methods used today that are more complex. In fact, across a number of tests of simulated robots with both 2D and 3D designs, and two and four-legged physical designs, the researchers were able to show that optimizations that would normally task as many as 30,000 simulations to achieve were instead possible in just 400.

Why is any of this even important? Because it basically shrinks drastically the amount of computational overhead required to get good movement results out of soft robots, which is a key ingredient in helping make them partial to actually use in real-life applications. If programming a soft robot to do something genuinely useful like navigate and effect an underwater damage assessment and repair requires huge amounts of processing power, and significant actual time, it's not really viable for anyone to actually deploy.

In the future, the research team hopes to bring their optimization method out of simulation and into real-world testing, as well as full-scale development of soft robots from start to finish.