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.
Recent News
Ashok Goel: CogSci 2022
Sungeun An: Presentation at The 23rd International Conference on Artificial Intelligence in Education.
Sungeun An: Presentation at ITS (Intelligence Tutoring System) 2022 conference
Faces of Research: Meet Ashok Goel
XPrize has selected Georgia Tech’s Veritas team for the round of 10 teams in the Digital Learning Challenge