Monday, May 20, 2024

Simpler Control For Shape-Shifting Robots

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The MIT team has developed a control algorithm that autonomously teaches a reconfigurable robot to move, stretch, and adapt its shape for specific tasks.

A new machine-learning technique can train and control a reconfigurable soft robot that can dynamically change its shape to complete a task. The researchers, from MIT and elsewhere, also built a simulator that can evaluate control algorithms for shape-shifting soft robots.
Credits:Image: Courtesy of the researchers; MIT News
A new machine-learning technique can train and control a reconfigurable soft robot that can dynamically change its shape to complete a task. The researchers, from MIT and elsewhere, also built a simulator that can evaluate control algorithms for shape-shifting soft robots. Credits:Image: Courtesy of the researchers; MIT News

Imagine a shape-shifting, slime-like robot designed to navigate through narrow spaces, with the potential to remove items from the human body. This innovative technology could revolutionize various fields, including healthcare and industrial systems, offering flexible and adaptive solutions. The main challenge is effectively developing a method to control this fluid, jointless machine.

The team at MIT have created a control algorithm that autonomously learns to move, stretch, and shape a reconfigurable robot to accomplish specific tasks, including those that require multiple morphological changes. Additionally, they developed a simulator to test control algorithms on various challenging tasks that demand the robot to alter its shape.

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Their method outperformed other algorithms by completing eight complex tasks. For example, the robot adjusted its size and shape to navigate through a narrow pipe and open its lid. Still, in early development, this technique shows potential for creating general-purpose robots that can adapt their forms to accomplish diverse tasks.

Controlling dynamic motion

To control a shape-shifting robot, the team has developed a reinforcement learning algorithm that starts by managing groups of muscles working together rather than individually. This approach uses a coarse-to-fine strategy, where the robot’s movements in an environment are treated like an image. Their model creates a 2D action space from environmental images and uses the material-point method for simulating motion, assigning points over grid-like pixels. This helps the algorithm recognize strong correlations between action points like image pixels, ensuring coordinated movements across different robot parts. The model also predicts optimal robot actions based on environmental analysis, increasing adaptability and efficiency.

Building a simulator

After developing their approach, the researchers created a simulation environment called DittoGym to test it. DittoGym challenges a reconfigurable robot with eight tasks, like weaving around obstacles or mimicking alphabet letters by changing shape. Their algorithm outperformed baseline methods and uniquely succeeded in multistage tasks requiring several shape transformations.

Nidhi Agarwal
Nidhi Agarwal
Nidhi Agarwal is a journalist at EFY. She is an Electronics and Communication Engineer with over five years of academic experience. Her expertise lies in working with development boards and IoT cloud. She enjoys writing as it enables her to share her knowledge and insights related to electronics, with like-minded techies.

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