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Speedy Robo-Gripper Reflexively Organizes Cluttered Spaces

Looking to give robots a more nimble, human-like touch, MIT engineers have developed a gripper that grasps by reflex. The team’s robot adapts in the moment to reflexively roll, palm, or pinch an object to get a better hold. Watch this video to see it carry out these “last centimeter” adjustments without engaging a higher-level planner.

“In environments where people live and work, there’s always going to be uncertainty,” says Andrew SaLoutos  , a graduate student in MIT’s Department of Mechanical Engineering. “Someone could put something new on a desk or move something in the break room or add an extra dish to the sink. We’re hoping a robot with reflexes could adapt and work with this kind of uncertainty.”

Topics:
Automation Motion Control Positioning Equipment Robotics

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    Transcript

    00:00:00 [MUSIC PLAYING] Andrew SaLoutos: Historically, a lot of the interest in robotic manipulation has been focused on pick and place in a factory environment, or in a more heavily structured environment. But if we want robots to go into the home, we need to be able to deal with more unstructured environments and places where people live. And if you look at human manipulation, something that sets us apart is our ability

    00:00:24 to react and use our local information at our fingertips. And we don't come up with these super complex manipulation plans ahead of time. A common approach in other manipulation systems is to use vision and use cameras and plan all the details, down to specific fingertip locations on an object. That slows down the system, and you depend heavily on how quickly you can get information from your cameras. So what we've done is we still use a vision system. And we have a camera basically take an image of the scene

    00:00:54 and say, here's the closest object that you can grasp. And for the camera, that's it. We've put proximity sensors and contact sensors in the fingers. So as it approaches the object, it can see the surfaces that it's coming up against and react around them. And then when it knows that something's in its grasp, it can close and measure the forces that it sees, so that it knows whether or not it's successfully grasped the object.

    00:01:16 Hongmin Kim: The cable driven mechanism is similar to our tendons in our fingers in that the cable drives the joints. So it can flex or it can extend at the joints. As opposed to other systems having actuators on the joints, we use a cable driven mechanism to place all the actuators at the base, which makes the joint really low inertia, which reduces the weight at the fingers, making the fingers really fast and reactive. Andrew SaLoutos: I guess the key way

    00:01:47 that our system is different is that if something goes wrong, so if the vision information is slightly off and the cup is not exactly where the camera thought it was, our system can react without having to consult the vision system again and ask for new information, or without ruining the entire manipulation plan. And we do that using only the local information on the fingertips. A key part of our approach is that we don't want to basically assemble a library

    00:02:14 of very individualized solutions for all of these objects. We want to grow our capabilities, but in a way that we stay generalizable. We didn't pick a single cup and design our controller for that cup. We want to sort of grasp this large class of cylindrical objects, and as we add more we want to keep that mindset.