How MIT is Making Robot Hands More Human-Like

MIT engineers have devised a way to give more dexterity to simple robotic grippers using the environment as a helping hand. Their model predicts the force with which a robotic gripper must push against surrounding fixtures in order to adjust its grasp.

Photo Caption: A simple robotic gripper can adjust its grip using the environment. Here, a robot grips a rod lightly while pushing it against a tabletop. This allows the rod to rotate in the robot’s fingers.

In this most recent paper, the group investigates an approach to extrinsic dexterity called “prehensile pushing” - exploiting fixtures in the environment to manipulate a grasped object.

“We’re sort of outsourcing that dexterity that you don’t have in the gripper to the environment and the arm,” Rodriguez explains. “Instead of dexterity that’s intrinsic to the hand, it’s extrinsic, in the environment.”

The researchers developed a model 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.

“Exploiting the Environment”

In its current iteration, the model predicts the force a gripper must exert, on the object and the environment, to maneuver 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 maneuver 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: A surgical robot may push a scalpel against an operating table to adjust its grip, while a forensic robot in the field may angle a piece of evidence against a nearby rock to better examine it.

“Exploiting the environment is, and will be, important for robots and the research community,” Rodriguez says. “Any applications where you have limitations in terms of payload or cost or complexity, areas like manufacturing, or surgery, or field operations, or even space exploration - whenever you have a gripper that is not dexterous like a human hand, this [method] gives you some of that dexterity.”

This research was supported, in part, by the National Science Foundation.




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