Robotic Grip Improved

Grip is a big thing in robotics.  This will be a big deal for classic smart home applications for eldercare.  And in factory manufacturing applications.

Giving robots a faster grasp
An algorithm speeds up the planning process robots use to adjust their grip on objects, for picking and sorting, or tool use.

Jennifer Chu | MIT News Office

If you’re at a desk with a pen or pencil handy, try this move: Grab the pen by one end with your thumb and index finger, and push the other end against the desk. Slide your fingers down the pen, then flip it upside down, without letting it drop. Not too hard, right?

But for a robot — say, one that’s sorting through a bin of objects and attempting to get a good grasp on one of them — this is a computationally taxing maneuver. Before even attempting the move it must calculate a litany of properties and probabilities, such as the friction and geometry of the table, the pen, and its two fingers, and how various combinations of these properties interact mechanically, based on fundamental laws of physics.

Now MIT engineers have found a way to significantly speed up the planning process required for a robot to adjust its grasp on an object by pushing that object against a stationary surface. Whereas traditional algorithms would require tens of minutes for planning out a sequence of motions, the new team’s approach shaves this preplanning process down to less than a second.

Alberto Rodriguez, associate professor of mechanical engineering at MIT, says the speedier planning process will enable robots, particularly in industrial settings, to quickly figure out how to push against, slide along, or otherwise use features in their environments to reposition objects in their grasp. Such nimble manipulation is useful for any tasks that involve picking and sorting, and even intricate tool use.

“This is a way to extend the dexterity of even simple robotic grippers, because at the end of the day, the environment is something every robot has around it,” Rodriguez says.

The team’s results are published today in The International Journal of Robotics Research. Rodriguez’ co-authors are lead author Nikhil Chavan-Dafle, a graduate student in mechanical engineering, and Rachel Holladay, a graduate student in electrical engineering and computer science.