Abstract
Learning robots that can acquire new motor skills and refine existing ones have been a long-standing vision of both robotics, and machine learning. However, off-the-shelf machine learning appears not to be adequate for robot skill learning, as it neither scales to anthropomorphic robotics nor do fulfills the crucial real-time requirements. As an alternative, we propose to divide the generic skill learning problem into parts that can be well-understood from a robotics point of view. In this context, we have developed machine learning methods applicable to robot skill learning. This paper discusses recent progress ranging from simple skill learning problems to a game of robot table tennis.
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Kober, J., Bagnell, D., Peters, J.: Reinforcement learning in robotics: A survey. International Journal of Robotics Research, IJRR (2013)
Nguyen-Tuong, D., Peters, J.: Model learning in robot control: a survey. Cognitive Processing (4) (2011)
Argall, B., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robotics and Autonomous Systems (2009)
Kober, J., Peters, J.: Imitation and reinforcement learning. IEEE Robotics and Automation Magazine (2010)
Peters, J.: Machine Learning of Motor Skills for Robotics. PhD thesis (2007)
Neumann, G., Peters, J.: Fitted Q-iteration by Advantage Weighted Regression. In: Advances in Neural Information Processing Systems 22, NIPS (2009)
Kober, J., Wilhelm, A., Oztop, E., Peters, J.: Reinforcement learning to adjust parametrized motor primitives to new situations. Autonomous Robots (2012)
Mülling, K., Kober, J., Krömer, O., Peters, J.: Learning to select and generalize striking movements in robot table tennis. IJRR (2013)
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Peters, J., Kober, J., Mülling, K., Krämer, O., Neumann, G. (2013). Towards Robot Skill Learning: From Simple Skills to Table Tennis. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_42
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DOI: https://doi.org/10.1007/978-3-642-40994-3_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40993-6
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