Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Robot Learning

  • Jan Peters
  • Russ Tedrake
  • Nicholas Roy
  • Jun Morimoto
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_732

Definition

Robot learning consists of a multitude of machine learning approaches, particularly  reinforcement learning,  inverse reinforcement learning and  regression methods. These methods have been adapted sufficiently to domain to achieve real-time learning in complex robot systems such as helicopters, flapping-wing flight, legged robots, anthropomorphic arms, and humanoid robots.

Robot Skill Learning Problems

In classical artificial intelligence-based robotics app-roaches, scientists attempted to manually generate a set of rules and models that allows the robot systems to sense and act in the real world. In contrast,  robot learning has become an interesting problem in robotics as (1) it may be prohibitively hard to program a robot for many tasks, (2) not all situations, as well as goals, may be foreseeable, and (3) real-world environments are often nonstationary (Connell and Mahadevan, 1993). Hence, future robots need to be able to adapt to the real world.

In comparison to many...

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Recommended Reading

  1. Recently, several special issues (Morimoto et al., 2010; Peters and Ng, 2009) and books (Sigaud, 2010) have covered the domain of robot learning. The classical book (Connell and Mahadevan, 1993) is interesting nearly 20 years after its publication. Additional special topics are treated in Apolloni et al. (2005) and Thrun et al. (2005).Google Scholar
  2. Apolloni, B., Ghosh, A., Alpaslan, F. N., Jain, L. C., & Patnaik, S. (2005). Machine learning and robot perception. Studies in computational intelligence (Vol. 7). Berlin: Springer.Google Scholar
  3. Coates, A., Abbeel, P., & Ng, A. Y. (2009). Apprenticeship learning for helicopter control. Communications of the ACM, 52(7), 97–105.CrossRefGoogle Scholar
  4. Connell, J. H., & Mahadevan, S. (1993). Robot learning. Dordrecht: Kluwer Academic.zbMATHGoogle Scholar
  5. Farrell, J. A., & Polycarpou, M. M. (2006). Adaptive approximation based control. Adaptive and learning systems for signal processing, communications and control series. Hoboken: John Wiley.Google Scholar
  6. Ham, J., Lin, Y., & Lee, D. D. (2005). Learning nonlinear appearance manifolds for robot localization. In International conference on intelligent robots and Systems, Takamatsu, Japan.Google Scholar
  7. Jenkins, O., Bodenheimer, R., & Peters, R. (2006). Manipulation manifolds: Explorations into uncovering manifolds in sensory-motor spaces (8 pages). In International conference on development and learning, Bloomington, INGoogle Scholar
  8. Kober, J., & Peters, J. (2009). Policy search for motor primitives in robotics. In Advances in neural information processing systems 22. Cambridge: MIT Press.Google Scholar
  9. Morimoto, J., Toussaint, M., & Jenkins, C. (2010). Special issue on robot learning in practice. IEEE Robotics and Automation Magazine, 17(2), 17–84.CrossRefGoogle Scholar
  10. Peters, J., & Ng, A. (2009). Special issue on robot learning. Autonomous Robots, 27(1–2):1–144.Google Scholar
  11. Peters, J., & Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4):682–697.CrossRefGoogle Scholar
  12. Riedmiller, M., Gabel, T., Hafner, R., & Lange, S. (July 2009). Reinforcement learning for robot soccer. Autonomous Robots, 27(1):55–73.CrossRefGoogle Scholar
  13. Schaal, S., Atkeson, C. G., & Vijayakumar, S. Scalable techniques from nonparameteric statistics for real-time robot learning. Applied Intelligence, 17(1):49–60.Google Scholar
  14. Schaal, S., Ijspeert, A., & Billard, A. (2003). Computational approaches to motor learning by imitation. Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358(1431):537–547.CrossRefGoogle Scholar
  15. Sigaud, O., & Peters, J. (2010). From motor learning to interaction learning in robots. Studies in computational intelligence (Vol. 264). Heidelberg: Springer.Google Scholar
  16. Tedrake, R., Zhang, T. W., & Seung, H. S. (2004). Stochastic policy gradient reinforcement learning on a simple 3d biped. In Proceedings of the IEEE international conference on intelligent robots and systems (pp. 2849–2854). IROS 2004, Sendai, Japan.Google Scholar
  17. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jan Peters
  • Russ Tedrake
  • Nicholas Roy
  • Jun Morimoto

There are no affiliations available