The weaknesses of existing learning techniques, and the variety of knowledge necessary to make a robot perform efficiently in the real world, suggest that many concurrent, complementary, and redundant learning methods are necessary. We propose a division of learning styles into four main types based on the amount of built-in structure and the type of information being learned. Using this classification, we discuss the effectiveness of various learning methodologies when applied in a real robot context.
- Mobile Robot
- Reinforcement Learning
- Real Robot
- Physical Robot
- Robot Learning
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© 1993 Springer Science+Business Media New York
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Brooks, R.A., Mataric, M.J. (1993). Real Robots, Real Learning Problems. In: Connell, J.H., Mahadevan, S. (eds) Robot Learning. The Springer International Series in Engineering and Computer Science, vol 233. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3184-5_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-6396-5
Online ISBN: 978-1-4615-3184-5
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