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Auke Jan Ijspeert
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 461
Citations - 22146
Auke Jan Ijspeert is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Robot & Humanoid robot. The author has an hindex of 65, co-authored 447 publications receiving 19103 citations. Previous affiliations of Auke Jan Ijspeert include École Normale Supérieure & University of Edinburgh.
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Journal ArticleDOI
2008 Special Issue: Central pattern generators for locomotion control in animals and robots: A review
TL;DR: Research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals, is reviewed.
Journal ArticleDOI
Dynamical movement primitives: Learning attractor models for motor behaviors
TL;DR: Dynamical movement primitives is presented, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques, and its properties are evaluated in motor control and robotics.
Journal ArticleDOI
From swimming to walking with a salamander robot driven by a spinal cord model.
TL;DR: A spinal cord model and its implementation in an amphibious salamander robot is presented that demonstrates how a primitive neural circuit for swimming can be extended by phylogenetically more recent limb oscillatory centers to explain the ability of salamanders to switch between swimming and walking.
Proceedings ArticleDOI
Movement imitation with nonlinear dynamical systems in humanoid robots
TL;DR: The results demonstrate that multi-joint human movements can be encoded successfully by the CPs, that a learned movement policy can readily be reused to produce robust trajectories towards different targets, and that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar.
Proceedings Article
Learning Attractor Landscapes for Learning Motor Primitives
TL;DR: By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability properties of the canonical system.