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Communication Dans Un Congrès Année : 2022

Temporal Alignment and Demonstration Selection as Pre-Processing Phase for Learning by Demonstration

Résumé

Robots can benefit from users’ demonstrations to learnmotions. To be efficient, a pre-processing phase needsto be performed on data recorded from demonstrations.This paper presents pre-processing methods developedfor Learning By Demonstration (LbD). Thepre-processing phase consists in methods composedof alignment algorithms and algorithms that select thegood demonstrations. In this paper we propose sixmethods and compare them to select the best one.

Dates et versions

hal-03670974 , version 1 (18-05-2022)

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Citer

Jérémie Donjat, Amélie Legeleux, Cédric Buche, Dominique Duhaut. Temporal Alignment and Demonstration Selection as Pre-Processing Phase for Learning by Demonstration. 35th International Florida Artificial Intelligence Research Society Conference (FLAIRS), May 2022, Hutchinson Island, United States. ⟨10.32473/flairs.v35i.130649⟩. ⟨hal-03670974⟩
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