A new algorithm significantly speeds 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 approach shaves this preplanning process down to less than a second. This faster 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.

Existing algorithms typically take hours to preplan a sequence of motions for a robotic gripper, mainly because for every motion that it considers, the algorithm must first calculate whether that motion would satisfy a number of physical laws such as Newton’s laws of motion and Coulomb’s law describing frictional forces between objects. A compact way to solve the physics of these manipulations in advance of deciding how the robot’s hand should move involves using “motion cones” that are essentially visual, cone-shaped maps of friction.

The inside of the cone depicts all the pushing motions that could be applied to an object in a specific location, while satisfying the fundamental laws of physics and enabling the robot to keep hold of the object. The space outside of the cone represents all the pushes that would in some way cause an object to slip out of the robot’s grasp. The algorithm calculates a motion cone for different possible configurations among a robotic gripper, an object that it is holding, and the environment against which it is pushing in order to select and sequence different feasible pushes to reposition the object.

The researchers tested the new algorithm on a physical setup with a three-way interaction in which a simple robotic gripper was holding a T-shaped block and pushing against a vertical bar. They used multiple starting configurations, with the robot gripping the block at a particular position and pushing it against the bar from a certain angle. For each starting configuration, the algorithm instantly generated the map of all the possible forces that the robot could apply and the position of the block that would result. The algorithm’s predictions reliably matched the physical outcome in the lab, planning out sequences of motions — such as reorienting the block against the bar before setting it down on a table in an upright position — in less than a second, compared with traditional algorithms that take more than 500 seconds to plan out.

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