Modeling of non-rigid object launching and manipulation is complex considering the wide range of dynamics affecting trajectory, many of which may be unknown. Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing. Recently, advancements in data-powered artificial intelligence methods have allowed learnable models and systems to emerge. It is desirable to train a model for launch prediction on a robot, as deep neural networks can account for immeasurable dynamics. However, the inability to collect large amounts of experimental data decreases performance of deep neural networks. Through estimating force coefficients, the accepted physics models can be leveraged to produce adequate supplemental data to artificially increase the size of the training set, yielding improved neural networks. In this paper, we introduce a new framework for algorithmic estimation of force coefficients for non-rigid object launching, which can be generalized to other domains, in order to generate large datasets. We implement a novel training algorithm and objective for our deep neural network to accurately model launch trajectory of non-rigid objects and predict whether they will hit a series of targets. Our experimental results demonstrate the effectiveness of using simulated data from force coefficient estimation and shows the importance of simulated data for training an effective neural network.

翻译：非刚性物体发射与操控的建模具有复杂性，因其轨迹受多种动力学因素影响，且其中许多因素可能未知。物理模型因无法考虑未知因素及物体发射过程中的形变效应而精度不足；此外，未经大量实验测试便无法推导这些模型的力系数。近年来，数据驱动型人工智能方法的进展催生了可学习模型与系统。由于深度神经网络能够考虑不可测量的动力学因素，在机器人上训练发射预测模型具有重要价值。然而，无法收集大量实验数据会降低深度神经网络的性能。通过估计力系数，可借助经典物理模型生成充足的补充数据以人为扩大训练集规模，从而优化神经网络性能。本文提出一种面向非刚性物体发射的力系数算法估计新框架，该框架可推广至其他领域，用于生成大规模数据集。我们为深度神经网络设计了新型训练算法与目标函数，使其能够精确建模非刚性物体的发射轨迹，并预测其能否命中系列目标。实验结果表明，基于力系数估计的仿真数据具有显著有效性，同时揭示了仿真数据对训练高效神经网络的关键作用。