https://www.sciencedaily.com/releases/2019/11/191121141427.htm

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.