Environment gives simple robotic grippers more dexterity
05 August 2015
Engineers at MIT have discovered a way to impart more dexterity to simple robotic grippers, using the environment to give a helping hand.
The team, led by Alberto Rodriguez, an assistant professor of mechanical engineering, and graduate student Nikhil Chavan-Dafle, has developed a model that predicts the force with which a robotic gripper needs to push against various fixtures in the environment in order to adjust its grasp on an object.
For instance, if a robotic gripper aims to pick up a pencil at its midpoint, but instead grabs hold of the eraser end, it could use the environment to adjust its grasp. Instead of releasing the pencil and trying again, Rodriguez’s model enables a robot to loosen its grip slightly, and push the pencil against a nearby wall, just enough to slide the robot’s gripper closer to the pencil’s midpoint.
Partnering robots with the environment to improve dexterity is an approach Rodriguez calls 'extrinsic dexterity' — as opposed to the intrinsic dexterity of, say, the human hand. To adjust one’s grip on a pencil in a similar fashion, a person, using one hand, could simply spider-crawl their fingers towards the centre of the pencil. But programming such intrinsic dexterity in a robotic hand is complex, significantly raising a robot’s cost.
“Chasing the human hand is still a very valid direction [in robotics],” Rodriguez says. “But if you cannot afford having a $100,000 hand that is very complex to use, this [method] brings some dexterity to very simple grippers.”
Rodriguez is currently exploring multiple ways in which the environment may be exploited to increase the dexterity of simple robotic grippers. In ongoing work, his group is looking for ways in which a robot might use gravity to toss and catch an object, as well as how surfaces like a tabletop may help a robot roll an object between its fingers.
The group is currently investigating an approach to extrinsic dexterity called 'prehensile pushing' — exploiting fixtures in the environment to manipulate a grasped object. A model has been developed that describes the forceful interaction between a gripper, a grasped object, and different types of external fixtures such as corners, edges, or surfaces.
To predict how an object may move as a gripper pushes it against a given fixture, the researchers designed the model to take into account various factors, including the frictional forces between the gripper and the object, and between the object and the environment, as well as the object’s mass, inertia, and shape.
In its current iteration, the model predicts the force a gripper must exert on the object and the environment to manoeuvre the object to a desired orientation. For instance, how tight should a robot grip a bar, and how hard must it push that bar against a point, to rotate the bar 45 degrees?
Rodriguez and Chavan-Dafle tested the model’s predictions against actual experiments, using a simple two-fingered gripper to manipulate a short rod, either rolling, pivoting, or sliding it against three fixtures: a point, a line, and a plane. The team measured the forces the robot exerted to manoeuvre the rod into the desired orientations, and compared the experimental forces with the model’s predicted forces.
“The agreement was pretty good,” Rodriguez says. “We’ve validated the model. Now we’re working on the planning side, to see how to plan motions to generate certain trajectories. One of the things we want to ask in the future is: how do you engineer fixtures in the environment so that a robot’s motions are more reliable, and can be executed faster?”
Ultimately, Rodriguez sees extrinsic dexterity as an inexpensive way to make simple robots more nimble for a variety of uses.
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