Recent success of machine learning in many domains has been overwhelming, which often leads to false expectations regarding the capabilities of behavior learning in robotics. In this survey, we analyze the current state of machine learning for robotic behaviors. We will give a broad overview of behaviors that have been learned and used on real robots. Our focus is on kinematically or sensorially complex robots. That includes humanoid robots or parts of humanoid robots, for example, legged robots or robotic arms. We will classify presented behaviors according to various categories and we will draw conclusions about what can be learned and what should be learned. Furthermore, we will give an outlook on problems that are challenging today but might be solved by machine learning in the future and argue that classical robotics and other approaches from artificial intelligence should be integrated more with machine learning to form complete, autonomous systems.

翻译：机器学习在诸多领域的近期成功令人瞩目，但这常常导致人们对机器人行为学习能力产生不切实际的期望。本综述分析了当前机器人行为学习的现状。我们将广泛概述已在真实机器人上实现并应用的学习行为，重点关注运动学或感知上具有复杂性的机器人，包括类人机器人或其组成部分（例如足式机器人或机械臂）。我们将依据不同类别对所介绍的行为进行分类，并就哪些内容能够学习以及哪些内容应当学习得出结论。此外，我们将展望当前具有挑战性但未来可能通过机器学习解决的问题，并论证应将经典机器人学及人工智能的其他方法与机器学习更紧密地结合，以构建完整的自主系统。