Bimanual regrasping from unimanual machine learning

Abstract

While unimanual regrasping has been studied extensively, either by regrasping in-hand or by placing the object on a surface, bimanual regrasping has seen little attention. The recent popularity of simple end-effectors and dual-manipulator platforms makes bimanual regrasping an important behavior for service robots to possess. We solve the challenge of bimanual regrasping by casting it as an optimization problem, where the objective is to minimize execution time. The optimization problem is supplemented by image processing and a unimanual grasping algorithm based on machine learning that jointly identify two good grasping points on the object and the proper orientations for each end-effector. The optimization algorithm exploits this data by finding the proper regrasp location and orientation to minimize execution time. Influenced by human bimanual manipulation, the algorithm only requires a single stereo image as input. The efficacy of the method we propose is demonstrated on a dual manipulator torso equipped with Barrett WAM arms and Barrett Hands.