An important prerequisite for autonomous robots is their ability to reliably grasp a wide variety of objects. Most state-of-the-art systems employ specialized or simple end-effectors, such as two-jaw grippers, which severely limit the range of objects to manipulate. Additionally, they conventionally require a structured and fully predictable environment while the vast majority of our world is complex, unstructured, and dynamic. This paper presents an implementation to overcome both issues. Firstly, the integration of a five-finger hand enhances the variety of possible grasps and manipulable objects. This kinematically complex end-effector is controlled by a deep learning based generative grasping network. The required virtual model of the unknown target object is iteratively completed by processing visual sensor data. Secondly, this visual feedback is employed to realize closed-loop servo control which compensates for external disturbances. Our experiments on real hardware confirm the system's capability to reliably grasp unknown dynamic target objects without a priori knowledge of their trajectories. To the best of our knowledge, this is the first method to achieve dynamic multi-fingered grasping for unknown objects. A video of the experiments is available at https://youtu.be/Ut28yM1gnvI.

翻译：自主机器人可靠抓取多种物体的能力是其重要前提。当前最先进的系统多采用专用或简单的末端执行器（如两爪夹持器），这严重限制了可操作物体的范围。此外，这类系统通常需要结构化和完全可预测的环境，而现实世界绝大部分是复杂、非结构化且动态变化的。本文提出了一种同时解决上述两个问题的实施方案。首先，集成五指手增加了可实现的抓取方式和可操作物体的多样性。这种运动学复杂的末端执行器由基于深度学习的生成式抓取网络控制。通过处理视觉传感器数据，逐步完善未知目标物体所需的虚拟模型。其次，利用这种视觉反馈实现闭环伺服控制，以补偿外部干扰。我们在真实硬件上的实验证实，该系统无需预先知晓物体运动轨迹，即可可靠抓取未知的动态目标物体。据我们所知，这是首个实现未知物体动态多指抓取的方法。实验视频见https://youtu.be/Ut28yM1gnvI。