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Robust Learning from Demonstrations using Multidimensional SAX
Yasser Mohammad,Toyoaki Nishida 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
Learning from demonstrations (LfD) is gaining more popularity in robotics due to its promise of providing a human-friendly technique for teaching robots new skills by robotics-naive users. The two main approaches to LfD are dynamic motor primitives (DMP) which models demonstrated motions as dynamical systems with the advantage flexibility in changing the motion’s starting position, goal or speed and Gaussian Mixture Modelling/ Gaussian Mixture Regression (GMM/GMR) which represents demonstrated motions as mixtures of Gaussians with the advantage of keeping track of the correlations between different dimensions of learned motions and automatic extraction of motion variability along these dimensions. This paper introduces a third approach that relies on symbolization of demonstrated motions by extending the Symbolic Aggregate approXimation (SAX) to handle multiple dimensions of data. The proposed approach is shown through several real-world evaluations to be more resistant to confusing demonstrations that usually arise when action segmentation is automated. The paper also discusses a possible way to combine SAX based LfD withGMM/GMR in order to preserve the advantages of these two approaches while providing superior confusion resistance.