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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1906.01868 |
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| _version_ | 1866917773947437056 |
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| author | Fabisch, Alexander Petzoldt, Christoph Otto, Marc Kirchner, Frank |
| author_facet | Fabisch, Alexander Petzoldt, Christoph Otto, Marc Kirchner, Frank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1906_01868 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives Fabisch, Alexander Petzoldt, Christoph Otto, Marc Kirchner, Frank Robotics Machine Learning 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. |
| title | A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/1906.01868 |