In an exciting development at MIT, researchers are working on creating robots unlike any we’ve seen before.
These aren’t your typical robots with stiff arms and legs; they are more like high-tech slime that can change their shape to fit through tight spaces.
This kind of technology could one day be used inside the human body to remove foreign objects, or for other tasks where flexibility and adaptability are crucial.
The big question is: How do you control a robot that is so flexible it can transform its entire body shape?
Traditional robots are easier to manage because they have clear parts like fingers or arms that can be moved in predictable ways.
But a shape-shifting robot is a whole different challenge.
The team at MIT, including an undergraduate from Tsinghua University in China and other experts, has come up with a smart solution.
They’ve created a special kind of computer program, or algorithm, that teaches the robot how to move and change form all by itself.
This algorithm uses a method called reinforcement learning, where the robot learns from trial and error, figuring out which actions help it accomplish a task.
Their approach is clever. Instead of trying to control each tiny part of the robot individually, they start by controlling bigger groups of parts.
Once the robot gets the hang of that, the algorithm refines its control, focusing on smaller and more specific areas. This method, called coarse-to-fine, is like sketching the big parts of a picture before adding in the small details.
To see if their method works, the researchers created a digital simulation environment called DittoGym. Here, they could test the robot in various scenarios, like navigating around obstacles or even mimicking the shapes of letters.
Each task was designed to challenge the robot in different ways, helping to improve the algorithm’s ability to adapt and respond.
The results were impressive. The MIT team’s technique performed better than other methods, particularly in complex tasks that required the robot to change shape multiple times.
For example, in one test, the robot had to adjust its height and grow tiny legs to move through a narrow space, then change back to reach and open a lid.
By treating the robot’s movements like a 2D image, where points on the robot are similar to pixels in a picture, the algorithm could better predict and control how those points move together. This insight helps the robot adapt smoothly to its surroundings.
While it might be a while before such robots are ready for real-world applications, the possibilities are exciting. The success of this project not only advances the field of soft robotics but could also inspire new ways to tackle other complex technological challenges.
As we look to the future, the work being done at MIT serves as a promising glimpse into the potential of robots that can adapt and transform to meet diverse needs, making them invaluable tools in medicine, industry, and beyond.
Source: MIT.
