In this article, we define “robotic contact juggling” to be the purposeful control of the motion of a 3-D smooth object as it rolls freely on a motion-controlled robot manipulator, or “hand.” While specific examples of robotic contact juggling have been studied before, in this article, we provide the first general formulation and solution method for the case of an arbitrary smooth object in a single-point rolling contact on an arbitrary smooth hand. Our formulation splits the problem into four subproblems: deriving the second-order rolling kinematics; deriving the 3-D rolling dynamics; planning rolling motions that satisfy the rolling dynamics and achieve the desired goal; and stabilization of planned rolling trajectories. The theoretical results are demonstrated in 3-D simulations and 2-D experiments using feedback from a high-speed vision system.

In this paper we propose a method for motion planning and feedback control of hybrid, dynamic, and non-prehensile manipulation tasks. We outline five subproblems to address this: determining a set of manipulation primitives, choosing a sequence of tasks, picking transition states, motion planning for each individual primitive, and stabilizing each mode using feedback control. We apply the framework to plan a sequence of motions for manipulating a block with a planar 3R manipulator. We demonstrate preliminary experimental results for a block resting on the manipulator with a desired goal state on a ledge outside of the robot's workspace. The planned primitives reorient the block using a series of fixed, rolling, and sliding contact modes, and throw it to the goal state.

This paper presents a framework for planning the motion of an η -fingered robot hand to create an inertial load on a grasped object to achieve a desired in-grasp sliding motion. The model of the sliding dynamics is based on a soft-finger limit surface contact model at each fingertip. A motion planner is derived to automatically solve for the finger motions for a given initial and desired configuration of the object relative to the fingers. Iterative planning and execution are shown to reduce the errors that occur due to the modeling and trajectory tracking errors. The framework is applied to the problem of regrasping a laminar object held in a pinch grasp. We propose a limited surface model of the contact pressure distribution at each finger to predict the sliding directions. Experimental validations are shown, including iterative error reduction and repeatability of the experiment.

This paper presents a framework for planning the motion of an n-fingered robot hand to create an inertial load on a grasped object to achieve a desired in-grasp sliding motion. The model of the sliding dynamics is based on a soft-finger limit surface contact model at each fingertip. The framework is applied to the problem of regrasping a block held in a pinch grasp. The approach is applied to two examples in simulation, one of which is tested experimentally.