Deep Learning in Robotics: Survey on Model Structures and Training Strategies
这篇综述梳理了机器人学中应用深度学习的主要挑战，分类介绍了成功解决方案，并讨论了何时选用模块化、单一模型或端到端深度学习，为选择模型结构和训练策略提供指导。
The ever-increasing complexity of robot applications induces the need for methods to approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of tools to address this kind of problems. This survey presents a categorization of the major challenges in robotics that leverage DL technologies and introduces representative examples of successful solutions for the described problems. We also consider the question when and whether to use modular, monolithic models or end-to-end DL, in order to provide a guideline for the selection of the correct model structure and training strategy. By doing so, the current role and adaptability of different techniques at different hierarchical levels of a robot-application can be highlighted, thus providing a well-structured basis to assist future approaches.