论文标题
多指抓住人类
Multi-Finger Grasping Like Humans
论文作者
论文摘要
如果我们能够正确指定该怎么做，那么带有多指抓手的机器人可以为我们执行高级操纵任务。在这项研究中，我们通过使机器人掌握像人类执行的抓握示范这样的物体来朝这个方向迈出一步。我们提出了一种基于优化的新方法，用于将人类掌握的示范转移到任何多指的握把上，该方法会产生模仿人手方向以及与物体的接触区域，同时减轻互渗渗；对Allegro和Barretthand Grippers进行的广泛实验表明，我们的方法导致掌握与人类示范的抓地力相比，而不是现有方法，而无需进行任何特定特定的调整。我们通过用户研究确认这些发现，并验证我们在真正的机器人上的适用性。
Robots with multi-fingered grippers could perform advanced manipulation tasks for us if we were able to properly specify to them what to do. In this study, we take a step in that direction by making a robot grasp an object like a grasping demonstration performed by a human. We propose a novel optimization-based approach for transferring human grasp demonstrations to any multi-fingered grippers, which produces robotic grasps that mimic the human hand orientation and the contact area with the object, while alleviating interpenetration. Extensive experiments with the Allegro and BarrettHand grippers show that our method leads to grasps more similar to the human demonstration than existing approaches, without requiring any gripper-specific tuning. We confirm these findings through a user study and validate the applicability of our approach on a real robot.
