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This paper addresses the problem of mixed initiative, shared control formaster-slave grasping and manipulation. We propose a novel system, in which anautonomous agent assists a human in teleoperating a remote slave arm/gripper,using a haptic master device. Our system is designed to exploit the humanoperator's expertise in selecting stable grasps (still an open research topicin autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robotkinematics, and ii) the desired post-grasp manipulative trajectory, are fed toan autonomous agent which transmits force cues to the human, to encouragemaximally manipulable grasp pose selections. Specifically, the autonomous agentprovides force cues to the human, during the reach-to-grasp phase, whichencourage the human to select grasp poses which maximise manipulationcapability during the post-grasp object manipulation phase. We introduce atask-relevant velocity manipulability cost function (TOV), which is used toidentify the maximum kinematic capability of a manipulator during post-graspmotions, and feed this back as force cues to the human during the pre-graspphase. We show that grasps which minimise TOV result in significantly reducedcontrol effort of the manipulator, compared to other feasible grasps. Wedemonstrate the effectiveness of our approach by experiments with both real andsimulated robots.

Human-in-the-loop optimisation: mixed initiative grasping for optimally facilitating post-grasp manipulative actions