论文标题
使用双重四基因复发神经网络预测刚体的动力学
Predicting Rigid Body Dynamics using Dual Quaternion Recurrent Neural Networks with Quaternion Attention
论文作者
论文摘要
我们提出了一种基于双重四个四面化的新型神经网络体系结构，该结构允许对信息进行紧凑的表示，主要重点是描述刚体运动。为了涵盖刚体运动固有的动态行为，我们提出了神经网络中的经常性体系结构。为了进一步对单个刚体之间的相互作用以及有效的外部输入之间的相互作用，我们结合了一种采用双重四基因代数的新型注意机制。引入的体系结构可通过基于梯度的算法进行训练。我们将方法应用于包裹预测问题，在包裹的预测问题中，具有初始位置，方向，速度和角速度的刚体通过固定的模拟环境移动，该环境在包裹和边界之间表现出丰富的相互作用。
We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of informations with a main focus on describing rigid body movements. To cover the dynamic behavior inherent to rigid body movements, we propose recurrent architectures in the neural network. To further model the interactions between individual rigid bodies as well as external inputs efficiently, we incorporate a novel attention mechanism employing dual quaternion algebra. The introduced architecture is trainable by means of gradient based algorithms. We apply our approach to a parcel prediction problem where a rigid body with an initial position, orientation, velocity and angular velocity moves through a fixed simulation environment which exhibits rich interactions between the parcel and the boundaries.
