Human motion intention based scaled teleoperation for orientation assistance in preshaping for grasping

In this paper, we present an algorithm that provides human motion intention based assistance to users teleoperating a remote gripper for preshaping over an object in order to grasp it. Human motion data from the remote arm is used to train a Hidden Markov Model (HMM) offline. During the execution of a grasping task, the motion data is processed in real time through the HMM to determine the intended preshape configuration of the user. Based on the intention, the motion of the remote arm is scaled up in those orientation directions that lead to the desired configuration, thus providing the necessary assistance to the user to preshape for grasping. Tests on healthy human subjects validated the hypothesis that the users are able to preshape quicker and with much ease. Average time savings of 36% were obtained.