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Publication Details

Reference TypeConference Proceedings
Author(s)Schaal, S.;Vijayakumar, S.;D'Souza, A.;Ijspeert, A.;Nakanishi, J.
TitleReal-time statistical learning for robotics and human augmentation
Journal/Conference/Book TitleInternational Symposium on Robotics Research
Keywordshumanoid robotics, statistical learning, movement primitives, real-time learning
AbstractReal-time modeling of complex nonlinear dynamic processes has become increasingly important in various areas of robotics and human augmentation. To address such problems, we have been developing special statistical learning methods that meet the demands of on-line learning, in particular the need for low computational complexity, rapid learning, and scalability to high-dimensional spaces. In this paper, we introduce a novel algorithm that possesses all the necessary properties by combining methods from probabilistic and nonparametric learning. We demonstrate the applicability of our methods for three different applications in humanoid robotics, i.e., the on-line learning of a full-body inverse dynamics model, an inverse kinematics model, and imitation learning. The latter application will also introduce a novel method to shape attractor landscapes of dynamical system by means of statis-tical learning.
Editor(s)Jarvis, R. A.;Zelinsky, A.
Place PublishedLorne, Victoria, Austrialia Nov.9-12
Short TitleReal-time statistical learning for robotics and human augmentation

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