In this paper we propose a new methodology for decision-making under
uncertainty using recent advancements in the areas of nonlinear stochastic
optimal control theory, applied mathematics, and machine learning. Grounded on
the fundamental relation between certain nonlinear partial differential
equations and forward-backward stochastic differential equations, we develop a
control framework that is scalable and applicable to general classes of
stochastic systems and decision-making problem formulations in robotics and
autonomy. The proposed deep neural network architectures for stochastic control
consist of recurrent and fully connected layers. The performance and
scalability of the aforementioned algorithm are investigated in three
non-linear systems in simulation with and without control constraints. We
conclude with a discussion on future directions and their implications to
robotics.

本文提出了一种基于非线性随机最优控制理论、应用数学和机器学习的不确定性决策制定新方法。我们开展了一项控制框架的研究，旨在解决机器人和自主决策问题中的不确定性，并提出了一种深度神经网络架构用于随机控制。在仿真非线性系统中，我们研究了所提算法的性能和可扩展性，并讨论了未来的研究方向及其对机器人技术的影响。