Deriving Dexterous, In-Hand Manipulation Primitives for Adaptive Robot Hands

Adaptive robot hands have changed the way we approach and think of robot grasping and manipulation. Traditionally, pinch, fingertip grasping and dexterous, in-hand manipulation tasks were executed with fully actuated, rigid robot hands and relied on analytic methods, computation of the hand object Jacobians and extensive numerical simulations for deriving optimal and minimal effort grasps. However, even insignificant uncertainties in the modeling space could render the extraction of candidate grasps or manipulation paths infeasible. Adaptive hands use underactuated mechanisms and structural compliance, facilitating by design the successful extraction of stable grasps and the robust execution of manipulation tasks, even under significant object pose or other environmental uncertainties. In this paper, we propose a methodology for the automated extraction of dexterous, in-hand manipulation strategies / primitives for adaptive hands. To do so, we use a constrained optimization scheme that describes the kinematics of adaptive hands during the grasping and manipulation processes, an automated experimental setup for data collection, a clustering technique that groups together similar manipulation strategies, and a dimensionality reduction technique that projects the robot kinematics to lower dimensional manifolds. In these manifolds, control is simplified and hand operation becomes more intuitive. In this work, we also assess the effect of the extracted manipulation primitives on the object pose perturbations. The efficiency of the proposed methods is experimentally verified for various adaptive robot hands. The extracted primitives can simplify the operation and control of the open-source robot hand designs of the Yale Open Hand project in dexterous manipulation tasks.