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Learning and Application of High-Level Concepts with Conceptual Spaces and PDDL

Robots should be able to learn skills from humans not only through kinesthetic teaching, but also by recognizing intentions and abstract concepts in human behavior. This work presents a method that enables robots to learn humandemonstrated concepts on an abstract, first-order logic based representational layer, while addressing the problems of keyframe extraction, concept learning, symbolic grounding and representation in PDDL.