论文标题
反应性的人向机器人的任意物体移交
Reactive Human-to-Robot Handovers of Arbitrary Objects
论文作者
论文摘要
在过去的十年中，人类机器人的物体移交是一个积极研究的机器人技术领域。但是，很少有技术和系统解决了以任意外观，大小，形状和刚性的不同物体交出的挑战。在本文中，我们提出了一个基于视觉的系统，该系统可以实现反应性的人向机器人对未知对象的移交。我们的方法将闭环运动计划与实时，暂时的掌握生成相结合，以确保反应性和运动平滑度。我们的系统在不同的对象位置和方向上是强大的，并且可以掌握刚性和非刚性对象。我们证明了方法在26种不同的家庭对象组成的新基准集中，我们的方法的普遍性，可用性和鲁棒性，幼稚用户（n = 6）的用户研究（n = 6）交出了15个对象的子集，以及一个系统的评估，检查了不同的方法。可以在https://sites.google.com/nvidia.com/handovers-of-arbitrary-objects上找到更多结果和视频。
Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and rigidity. In this paper, we present a vision-based system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally-consistent grasp generation to ensure reactivity and motion smoothness. Our system is robust to different object positions and orientations, and can grasp both rigid and non-rigid objects. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects. More results and videos can be found at https://sites.google.com/nvidia.com/handovers-of-arbitrary-objects.
