An empirical comparison among the effect of different supports in sequential robotic manipulation

Abstract

Pick-and-place regrasp extends the manipulation capability of a robot by using a sequence of regrasps to accomplish tasks that are not possible using a single grasp due to constraints such as kinematics or collisions between the robot and the environment. Previous work on pick-and-place only leveraged static passive devices for intermediate placements, and thus is limited in the flexibility and robustness to reorient an object. In this paper, we extend the reorientation capability of a pick-and-place regrasp by adding an actively actuated gripper fixed in the working cell, and using it as the intermediate location for regrasping. In particular, our method automatically computes the stable placements of an object being hold in the gripper support, finds a rich set of force-closure grasps, performs k-means based grasp clustering, generates a graph of regrasp actions, and searches for the optimal regrasp sequence. To compare the regrasping performance with typical passive supports, we evaluate the success rate while performing tasks on various models. Experiments on reorientation tasks validate the benefit of using an actively actuated gripper for regrasp placement.