Abstract
This chapter illustrates, with a case study from the robotized assembly domain, the importance of feature transformation. The specific problem that is addressed is learning failure diagnosis models for a pick-and-place operation. Several feature transformation strategies are evaluated on flat as well as hierarchical learning problems. The SKIL learning algorithm, previously proposed by the authors, is used in most experiments. A comparison with an oblique tree learning algorithm is also included.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Botta, M. and Giordana, A. (1993). Smart+: A multi-strategy learning tool. In Proc. International Joint Conference on Artificial Intelligence, pages 937–943.
Bracewell, R. (1986). The Fourier Transform and its Applications. McGraw-Hill, Singapore.
Breiman, L., Friedman, J., Oleshen, R., and Stone, C. (1984). Classification and Regression Trees. Wadsforth International Group.
Camarinha-Matos, L., Seabra Lopes, L., and Barata, J. (1996). Integration and learning in supervision of flexible assembly systems. IEEE Transactions on Robotics and Automation, 12:202–219.
Kaiser, M., Giordana, A., Nuttin, M., and Seabra Lopes, L. (1995). B-learn ii: Combining sensing and action (final project report: Short version). Technical report, ESPRIT BRA 7274 B-LEARN II.
Koller, D. and Sahami, M. (1997). Hierarchically classifying documents using very few words. In Proceedings of the International Conference on Machine Learning, pages 170–178.
Kuniyoshi, Y., Inaba, M., and Inoue, H. (1994). Learning by watching: Extracting reusable task knowledge from visual observation of human performance. IEEE Transactions on Robotics and Automation, 10:799–822.
Michalski, R. (1983). A theory and methodology of inductive learning. Artificial Intelligence, 20:111–161.
Murthy, S., Kasif, S., Salzberg, S., and Beigel, R. (1993). Ocl: Randomized induction of oblique decision trees. In Proceedings of the Eleventh National Conference on Artificial Intelligence.
Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1 : 81–106.
Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco, California.
Seabra Lopes, L. (1997). Robot Learning at the Task Level. A Case Study in the Assembly Domain (Ph.D. Thesis). Universidade Nova de Lisboa, Portugal.
Seabra Lopes, L. and Camarinha-Matos, L. (1995). Inductive generation of diagnostic knowledge for autonomous assembly. In Proc. IEEE International Conference on Robotics and Automation, pages 2545–2545.
Seabra Lopes, L. and Camarinha-Matos, L. (1996). Learning failure recovery knowledge for mechanical assembly. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media New York
About this chapter
Cite this chapter
Lopes, L.S., Camarinha-Matos, L.M. (1998). Feature Transformation Strategies for a Robot Learning Problem. In: Liu, H., Motoda, H. (eds) Feature Extraction, Construction and Selection. The Springer International Series in Engineering and Computer Science, vol 453. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5725-8_23
Download citation
DOI: https://doi.org/10.1007/978-1-4615-5725-8_23
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7622-4
Online ISBN: 978-1-4615-5725-8
eBook Packages: Springer Book Archive