Dynamic Grasping with Reachability and Motion Awareness

Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning needs to be adaptive and in real time, the delay in computation makes prediction necessary. In this paper, we present a dynamic grasping framework that is reachability-aware and motion-aware. Specifically, we model the reachability space of the robot using a signed distance field which enables us to quickly screen unreachable grasps. Also, we train a neural network to predict the grasp quality conditioned on the current motion of the target. Using these as ranking functions, we quickly filter a large grasp database to a few grasps in real time. In addition, we present a seeding approach for arm motion generation that utilizes solution from previous time step. This quickly generates a new arm trajectory that is close to the previous plan and prevents fluctuation. We implement a recurrent neural network (RNN) for modelling and predicting the object motion. Our extensive experiments demonstrate the importance of each of these components and we validate our pipeline on a real robot.

在动态环境中抓取面临一系列独特的挑战。当目标物体移动时，稳定且可及的抓取可能变得不可及且不稳定，运动规划需要具有适应性和实时性，计算的延迟使得预测成为必要。在本文中，我们提出了一种具有可达性感知和运动感知的动态抓取框架。具体而言，我们使用有符号距离场对机器人的可达空间进行建模，这使我们能够快速筛选出不可及的抓取。此外，我们训练了一个神经网络，根据目标的当前运动来预测抓取质量。利用这些作为排序函数，我们实时地将一个大型抓取数据库快速筛选为少数几个抓取。另外，我们提出了一种用于手臂运动生成的种子方法，该方法利用前一个时间步的解。这能快速生成一个接近先前计划的新手臂轨迹，并防止波动。我们实现了一个循环神经网络（RNN）来对物体运动进行建模和预测。我们大量的实验证明了这些组件中每一个的重要性，并且我们在一个真实的机器人上验证了我们的流程。