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MIT Research News

November 22, 2019

How to design and control robots with stretchy, flexible bodies

Optimizing soft robots to perform specific tasks is a huge computational problem, but a new model can help.

Rob Matheson | MIT News Office

MIT researchers have invented a way to efficiently optimize the control and design of soft robots for target tasks, which has traditionally been a monumental undertaking in computation.

Soft robots have springy, flexible, stretchy bodies that can essentially move an infinite number of ways at any given moment. Computationally, this represents a highly complex “state representation,” which describes how each part of the robot is moving. State representations for soft robots can have potentially millions of dimensions, making it difficult to calculate the optimal way to make a robot complete complex tasks.

At the Conference on Neural Information Processing Systems next month, the MIT researchers will present a model that learns a compact, or “low-dimensional,” yet detailed state representation, based on the underlying physics of the robot and its environment, among other factors. This helps the model iteratively co-optimize movement control and material design parameters catered to specific tasks.