人在回路优化：混合主动抓取以最佳地促进抓取后操纵动作
Human-in-the-loop optimisation: mixed initiative grasping for optimally facilitating post-grasp manipulative actions
摘要：
研究了主从机器人混合主动、共享控制的抓取与操作问题。我们提出了一种新的系统，其中一个自主智能体协助人类在远程操作的从臂/抓手，使用触觉主设备。我们的系统旨在利用人类操作员的专业知识，在选择稳定的把握（仍然是一个开放的研究课题，在自主机器人）。同时，先验知识：i）从机器人运动学，和ii）所需的后把握操纵轨迹，被馈送到一个自主智能体，它将力的线索，以鼓励最大限度地操纵把握姿势选择。具体地，自主智能体在到达抓取阶段期间向人类提供力提示，这鼓励人类选择在抓取后对象操纵阶段期间使操纵能力最大化的抓取姿势。我们引入了一个任务相关的速度可操作性成本函数（TOV），这是用来确定最大的运动能力的机械手在后把握运动，并反馈这作为力线索的人在预把握阶段。我们表明，把握，最大限度地减少TOV的结果显着减少控制工作的机械手，相比其他可行的把握。我们证明了我们的方法的有效性与真实和模拟机器人的实验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
