In this paper, we explore the dynamic grasping of moving objects through active pose tracking and reinforcement learning for hand-eye coordination systems. Most existing vision-based robotic grasping methods implicitly assume target objects are stationary or moving predictably. Performing grasping of unpredictably moving objects presents a unique set of challenges. For example, a pre-computed robust grasp can become unreachable or unstable as the target object moves, and motion planning must also be adaptive. In this work, we present a new approach, Eye-on-hAnd Reinforcement Learner (EARL), for enabling coupled Eye-on-Hand (EoH) robotic manipulation systems to perform real-time active pose tracking and dynamic grasping of novel objects without explicit motion prediction. EARL readily addresses many thorny issues in automated hand-eye coordination, including fast-tracking of 6D object pose from vision, learning control policy for a robotic arm to track a moving object while keeping the object in the camera's field of view, and performing dynamic grasping. We demonstrate the effectiveness of our approach in extensive experiments validated on multiple commercial robotic arms in both simulations and complex real-world tasks.

翻译：本文探索了通过主动姿态跟踪和强化学习实现手眼协调系统中动态抓取运动物体的方法。现有的大多数基于视觉的机器人抓取方法隐式假设目标物体静止或以可预测方式运动。对不可预测运动物体进行抓取面临一系列独特挑战：例如，预计算的鲁棒抓取姿态可能因目标物体运动而变得不可达或不稳定，且运动规划也必须具备适应性。本研究提出了一种新方法——眼在手上强化学习器（EARL），使耦合式眼在手上（EoH）机器人操作系统能够在无需显式运动预测的情况下，对未知物体进行实时主动姿态跟踪和动态抓取。EARL有效解决了自动手眼协调中的诸多棘手问题，包括：基于视觉的6D物体姿态快速跟踪、学习机械臂在保持目标物体位于相机视野内的同时跟踪运动物体的控制策略，以及执行动态抓取。通过在多个商用机械臂上开展的大量实验（涵盖仿真环境与复杂真实世界任务），我们验证了该方法的有效性。