Soft robots that hold the right amount of force |  MIT news

Soft robots that hold the right amount of force | MIT news

The use of tools has long been a hallmark of human intelligence, as well as a practical problem for solving a wide range of robotic applications. But the machines are still unwilling to exert the right amount of force to control tools that are not tightly attached to their hands.

To manipulate said tools more powerfully, researchers from the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence (CSAIL) Laboratory, in collaboration with the Toyota Research Institute (TRI), have designed a system that can accommodate the tools and apply the appropriate amount of force for a particular task, such as scanning a liquid or writing a word By.

The system is dubbed Flexible Terminal Effector Series, or SEED, soft bubble grips and built-in cams to map how the grippers deform over a hexagonal space (think of an airbag inflating and deflation) and apply force to the tool. With six degrees of freedom, the object can be moved left and right, up or down, back and forth, roll, pitch, and yaw. The Closed-Loop Controller – a self-regulating system that maintains the desired state without human intervention – uses SEED and visual feedback to adjust the position of the robot arm in order to apply the desired force.

This may be useful, for example, for someone who uses tools when there is uncertainty in the height of the table, because the preprogrammed path may miss the table altogether. says Hyung Joo Suh, a doctoral student in electrical engineering and computer science at MIT, a CSAIL partner, and lead author on New paper on SEED. “Here’s the idea, if you really have three dimensions to move around in while you’re writing on the board, you want to be able to control position on some axes, while controlling force on the other axle.”

Only hard-bodied robots and their counterparts can take us so far; Smoothness and compliance provide luxury and the ability to deform, sensing the interaction between the tool and the hand.

With SEED, every execution the robot senses is a fresh 3D image of the grippers, thus tracking in real time how the grippers change shape around an object. These images are used to reconstruct the position of the tool, and the robot uses a learned model to map the position of the tool to the measured force. The learned model is obtained using the robot’s previous experience, perturbing the torque sensor to see how hard the bubble clutch is. Now, once the robot senses force, it will compare that to the force the user is commanding, and might say to itself, “It turns out the force I’m feeling right now isn’t quite there. I need to push harder.” Then it moves in the direction to increase the force, and it’s all done on a 6D space.

During the “squeegee mission,” SEED was fitted with just the right amount of power to wipe some fluid on the plane, as baseline methods struggled to get the scan right. When asked to put the paper on the pen, the robot effectively wrote “MIT,” and was also able to apply the right amount of force to push the screw.

While SEED was aware of the fact that it needed to control force or torque for a particular task, if it was grasped tightly, the item would inevitably slip, so there is an upper limit to that hardness being exerted. Also, if you are a hard robot, you can simulate systems that are softer than natural mechanical hardness – but not the other way around.

Currently, the system assumes a very specific geometry of the tools: they have to be cylindrical, and there are still many limitations on how they generalize when they conform to new types of shapes. Upcoming work may involve generalizing the framework to various forms so that it can handle arbitrary tools in the wild.

“No one would be surprised that compliance can help with tools, or that force sensing is a good idea; the question here is where compliance should go on a robot and how thin it should be,” says research co-author Ross Tedrick, Toyota Professor for Electrical and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT and Investigative Director at CSAIL. “Here we explore very soft stiffness regulation with six degrees of freedom directly on the hand/tool ​​interface, and show that there are some great advantages to doing so.”

Suh wrote the paper along with Naveen Kubuswamy, a senior researcher at the Toyota Research Institute. Tao Pang, PhD student in mechanical engineering at MIT and its affiliate CSAIL; Paul Mitegui and Alex Alspach of TRI; And Tedrick. They will present work at the IEEE/RSJ International Conference on Intelligent Robots and Systems Conference in October.

The Toyota Research Institute provided funds to support this work.

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