The simplicity of the visual servoing approach makes it an attractive option for tasks dealing with vision-based control of robots in many real-world applications. However, attaining precise alignment for unseen environments pose a challenge to existing visual servoing approaches. While classical approaches assume a perfect world, the recent data-driven approaches face issues when generalizing to novel environments. In this paper, we aim to combine the best of both worlds. We present a deep model predictive visual servoing framework that can achieve precise alignment with optimal trajectories and can generalize to novel environments. Our framework consists of a deep network for optical flow predictions, which are used along with a predictive model to forecast future optical flow. For generating an optimal set of velocities we present a control network that can be trained on the fly without any supervision. Through extensive simulations on photo-realistic indoor settings of the popular Habitat framework, we show significant performance gain due to the proposed formulation vis-a-vis recent state-of-the-art methods. Specifically, we show a faster convergence and an improved performance in trajectory length over recent approaches.

中文翻译：

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DeepMPCVS：用于视觉伺服的深度模型预测控制

视觉伺服方法的简单性使其成为许多现实应用中处理基于视觉的机器人控制任务的有吸引​​力的选择。然而，对于看不见的环境实现精确对准对现有的视觉伺服方法提出了挑战。虽然经典方法假定了一个完美的世界，但是当推广到新颖的环境时，最近的数据驱动方法面临一些问题。在本文中，我们旨在将两全其美相结合。我们提出了一种深度模型预测视觉伺服框架，该框架可以实现与最佳轨迹的精确对准，并且可以推广到新颖的环境。我们的框架由用于光流预测的深度网络组成，该网络与预测模型一起用于预测未来的光流。为了生成最佳速度集，我们提出了一个控制网络，该网络可以在没有任何监督的情况下进行实时训练。通过对流行的“人居”框架的逼真的室内设置进行广泛的模拟，由于相对于最新技术水平的拟议配方，我们显示出显着的性能提升。具体而言，我们展示了较新方法更快的收敛性和轨迹长度的改进性能。