Contextually Dependent Control Strategies for Manipulation

Traditional analytic robotics defines grasping by knowing the task geometry and the forces acting on the manipulator precisely. This method is particularly important for non-compliant manipulators with few degrees of freedom, such as a parallel jaw gripper, that overconstrain the solution space. In contrast, the advent of anthropomorphic, high degree-of-freedom grippers allows us to use closed-loop strategies that depend heavily on the task context but do not require precise positioning knowledge. To demonstrate, a robotic hand flips a plastic egg, using the finger joint tendon tensions as the sole control signal. The manipulator is a compliant, sixteen degree-of-freedom, Utah/MIT hand mounted on a Puma 760 arm. The completion of each subtask, such as picking up the spatula, finding the pan, and sliding the spatula under the egg, is detected by sensing when the tensions of the hand tendons pass a threshold. Beyond this use of tendon tensions and the approximate starting position of the spatula and pan, no model of the task is constructed. The routine is found to be robust to different spatulas and to changes in the location and orientation of the spatula, egg, and table, with some exceptions. .pp The egg-flipping example relies on interpreting fluctuating tension values within a known temporal sequence of actions. For instance, knowing when the manipulator is trying to touch the pan with the spatula provides the context to interpret changes in tendon tensions. Given the success of this task, we go on to propose a method for analyzing the temporal sensory output for tasks that have not been previously segmented. This method suggests a means for automatically generating robust force-control programs to perform previously teleoperated manipulation tasks.