Imitation is a well known method for learning. Case-based reasoning is an important paradigm for imitation learning; thus, case retrieval is a necessary step in case-based interpretation of skill demonstrations. In the context of a case-based robot that learns by imitation, each case may represent a demonstration of a skill that a robot has previously observed. Before it may reuse a familiar, source skill demonstration to address a new, target problem, the robot must first retrieve from its case memory the most relevant source skill demonstration. We describe three techniques for visual case retrieval in this context: feature matching, feature transformation matching, and feature transformation matching using fractal representations. We found that each method enables visual case retrieval under a different set of conditions pertaining to the nature of the skill demonstration.
CITATION STYLE
Fitzgerald, T., McGreggor, K., Akgun, B., Thomaz, A., & Goel, A. (2015). Visual case retrieval for interpreting skill demonstrations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9343, pp. 119–133). Springer Verlag. https://doi.org/10.1007/978-3-319-24586-7_9
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