One of the most commonly performed manipulation in a human's daily life is pouring. Many factors have an effect on target accuracy, including pouring velocity, rotation angle, geometric of the source, and the receiving containers. This paper presents an approach to increase the repeatability and accuracy of the robotic manipulator by estimating the change in the amount of water of the pouring cup to a sequence of pouring actions using multiple layers of the deep recurrent neural network, especially gated recurrent units (GRU). The proposed GRU model achieved a validation mean squared error as low as 1e-4 (lbf) for the predicted value of weight f(t). This paper contains a comprehensive evaluation and analysis of numerous experiments with various designs of recurrent neural networks and hyperparameters fine-tuning.

翻译：人类日常生活中最常操作的操纵之一正在倾斜,许多因素对目标准确性有影响,包括倾斜速度、旋转角度、源的几何和接收容器,本文件提出了提高机器人操纵器的重复性和准确性的一种方法,即估计倒杯水量的变化,以一系列倾注行动为手段,使用深层的经常性神经网络多层,特别是闭门的经常性单元(GRU),拟议的GRU模型取得了一个验证平均正方形错误,其重量的预测值为1e-4(lbf),低至1e-4(lbf),本文载有对多项试验的全面评价和分析,这些试验涉及经常性神经网络和超分光计微调的各种设计。