论文标题
抓住作为推理：在严重混乱的环境中反应性抓握
Grasping as Inference: Reactive Grasping in Heavily Cluttered Environment
论文作者
论文摘要
尽管在通过数据驱动方法掌握的任务中，证明了闭环反馈和预测6个自由度（DOF）掌握度，而不是常规使用的4DOF自上而下的掌握可以单独提高性能，但很少有系统都具有。此外，该任务的顺序属性几乎无法处理，而接近的运动必然会产生一系列观察结果。因此，本文综合了三种方法，并提出了一个闭环框架，该框架可以通过连续收到的视力观察结果在严重混乱的环境中预测6DOF的掌握。可以通过将抓地力问题作为隐藏的马尔可夫模型来实现，并应用粒子过滤器来推断掌握。此外，我们引入了一种新型的轻质卷积神经网络（CNN）模型，该模型可实时评估和初始化样品，从而使粒子滤波器过程成为可能。这些实验是在具有严重混乱环境的真实机器人上进行的，它表明，与基线算法相比，我们的框架不仅可以显着提高握把的成功率，而且对环境的动态变化和清理桌子的动态变化做出了反应。
Although, in the task of grasping via a data-driven method, closed-loop feedback and predicting 6 degrees of freedom (DoF) grasp rather than conventionally used 4DoF top-down grasp are demonstrated to improve performance individually, few systems have both. Moreover, the sequential property of that task is hardly dealt with, while the approaching motion necessarily generates a series of observations. Therefore, this paper synthesizes three approaches and suggests a closed-loop framework that can predict the 6DoF grasp in a heavily cluttered environment from continuously received vision observations. This can be realized by formulating the grasping problem as Hidden Markov Model and applying a particle filter to infer grasp. Additionally, we introduce a novel lightweight Convolutional Neural Network (CNN) model that evaluates and initializes grasp samples in real-time, making the particle filter process possible. The experiments, which are conducted on a real robot with a heavily cluttered environment, show that our framework not only quantitatively improves the grasping success rate significantly compared to the baseline algorithms, but also qualitatively reacts to a dynamic change in the environment and cleans up the table.
