New method to give robots more dexterity

Engineers at MIT, including one of Indian-origin, have found a way to impart more dexterity to simple robotic grippers using the environment as a helping hand.
The researchers from Massachusetts Institute of Technology 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, the new 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.

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.
Partnering robots with the environment to improve dexterity is an approach researchers call "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 her fingers towards the centre of the pencil. But programming such intrinsic dexterity in a robotic hand is extremely tricky, significantly raising a robot's cost.

With the new approach, developed by Alberto Rodriguez, an assistant professor of mechanical engineering and graduate student Nikhil Chavan-Dafle, existing robots in manufacturing, medicine, disaster response, and other gripper-based applications may interact with the environment, in a cost-effective way, to perform more complex manoeuvres.
"Chasing the human hand is still a very valid direction [in robotics]," Rodriguez said.
"But if you cannot afford having a USD 100,000 hand that is very complex to use, this [method] brings some dexterity to very simple grippers," he said.
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. We've validated the model. Now we're working on the planning side, to see how to plan motions to generate certain trajectories," Rodriguez said.