Abstract
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.
Highlights
C OMPUTERS can solve formal problems that are demanding for humans
We especially focus on the inhierarchy scope of some approaches, along with their training strategies, and argue that larger deep learning (DL) models that incorporate multiple levels of the hierarchy presented in Fig. 1, such as end-to-end methods, are not always preferable over modular DL-based solutions
For example end-to-end DL is often associated with the model structure, meaning that a single DL model performs the task, but it is frequently used to describe the training strategy when the model is trained as a whole
Summary
C OMPUTERS can solve formal problems that are demanding for humans. The increasing need for adaptive systems requires the solution of tasks that are hard to formulate, but can be solved by humans, such as the recognition and manipulation of objects. In order to perform such tasks, a certain complex knowledge of the environment is inevitable. The automatic extraction of the required knowledge is called machine learning (ML). The way the data is presented to the ML system, heavily influences how well the extracted knowledge represents the given problem. The ML approaches that perform feature extraction, using multiple hierarchical artificial neural network layers, are referred to as deep learning (DL) [1], [2]
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