把持後の操作を最適化する人間参加型の共有制御グリッピング
Human-in-the-loop optimisation: mixed initiative grasping for optimally facilitating post-grasp manipulative actions
遠隔操作で把持する際、把持後の操作しやすさを評価する指標を力覚フィードバックで人間に提示し、操作しやすい把持姿勢の選択を支援するシステムを提案・実機とシミュレーションで検証した。
著者: Amir M. Ghalamzan Esfahani, Firas Abi-Farraj, Paolo Robuffo Giordano, Rustam Stolkin
分類: cs.RO
原文アブストラクト
This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasps (still an open research topic in autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robot kinematics, and ii) the desired post-grasp manipulative trajectory, are fed to an autonomous agent which transmits force cues to the human, to encourage maximally manipulable grasp pose selections. Specifically, the autonomous agent provides force cues to the human, during the reach-to-grasp phase, which encourage the human to select grasp poses which maximise manipulation capability during the post-grasp object manipulation phase. We introduce a task-relevant velocity manipulability cost function (TOV), which is used to identify the maximum kinematic capability of a manipulator during post-grasp motions, and feed this back as force cues to the human during the pre-grasp phase. We show that grasps which minimise TOV result in significantly reduced control effort of the manipulator, compared to other feasible grasps. We demonstrate the effectiveness of our approach by experiments with both real and simulated robots.
