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UMINF 08.10

Formalising Learning from Demonstration

The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. Inspired by the work on planning and actuation by LaValle, common LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as the mappings between some of these spaces. Finally, behavior primitives are introduced as one example of useful bias in the learning process, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination.

Keywords

Action selection, Behavior, Bias, Generalization, Goal, Learning from Demonstration, Robot Learning, Segmentation

Authors

Erik Billing and Thomas Hellström

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Page Responsible: Frank Drewes
2022-09-30