Researchers at the MIT Computer Science and Artificial Intelligence Laboratory and Harvard’s Wyss Institute have developed soft robotic muscles that can lift up to 1,000 times their own weight. The technology is inspired by the Japanese art of paper folding – it uses an origami-like skeleton encased in an air- or liquid-filled bag. To get the muscle to expand or contract like an arm, one need only reduce or increase the pressure inside the bag.
The soft robot’s internal skeleton can be constructed from a variety of materials, and its range of flexibility and motion is determined by its folds. While this means that the soft robots cannot be reprogrammed once their “folds” have been put in place, as The Verge writes, it’s not really a major limitation. Indeed, algorithms can be used to find origami patterns that fold in near-infinite ways, including more complex motions such as twisting. The low cost (muscles can be built from a range of affordable, readily available materials) and speed of production also mean that they can be quickly fabricated and easily repaired to suit.
The soft robot muscles can also be constructed in a range of sizes from a centimeter to a meter to increase strength—i.e. the bigger the muscle, the bigger the lift—and joined together to create more elaborate systems. As seen above, a combination of four muscles forms an arm and a grip that can pick up a tire. Additional muscles could be added to offer horizontal movement to the lift, allowing the tire to be placed in different locations.
Researchers see the soft robot technology being applied to medical assistance devices, space exploration, wearable exoskeletons, and of course, within warehouses and logistic operations, where they could handle fragile or unusually shaped objects. Researchers are also in the midst of building an elephant trunk “as flexible and powerful” as the real thing. As Professor Daniela Rus, CSAIL’s director, told Wired, “I like the elephant trunk because it’s such a sophisticated manipulation mechanism.”
Via The Verge
Images via MIT CSAIL