Title :
Pattern-preserving-based motion imitation for robots
Author :
Shin, Bonggun ; Jo, Sungho
Author_Institution :
Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
Abstract :
This paper presents a new algorithm of encoding dynamic movements through pattern-preserving optimization by a physical robot. This research follows a recent robot programming approach called learning from demonstration in which the motion trajectory is learned from human demonstrations. The motivation of this work is to deal with major challenges in learning from demonstration such as embodiment mapping, generalization, adaptation, robustness to perturbations, stability, pattern-preserving, and parameter tuning. We propose a new method that can deal with those problems and present empirical results to support our insistence.
Keywords :
control engineering computing; learning (artificial intelligence); robot dynamics; robot programming; stability; adaptation; embodiment mapping; encoding dynamic movements; generalization; human demonstrations; learning; motion trajectory; parameter tuning; pattern-preserving optimization; pattern-preserving-based motion imitation; perturbations; physical robot; robot programming approach; stability; Asymptotic stability; Optimization; Robots; Robustness; Shape; Stability analysis; Trajectory; learning from demonstration; motion imitation; pattern-preserving optimization;
Conference_Titel :
Ubiquitous Robots and Ambient Intelligence (URAI), 2011 8th International Conference on
Conference_Location :
Incheon
Print_ISBN :
978-1-4577-0722-3
DOI :
10.1109/URAI.2011.6145926