Statistical dynamical systems for skills acquisition in humanoids

Sylvain Calinon, Zhibin Li, Tohid Alizadeh, Nikos G Tsagarakis, Darwin G Caldwell

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Learning by imitation in humanoids is challenging due to the unpredictable environments these robots have to face during reproduction. Two sets of tools are relevant for this purpose: 1) probabilistic machine learning methods
that can extract and exploit the regularities and important features of the task; and 2) dynamical systems that can cope with perturbation in real-time without having to replan the whole movement. We present a learning by imitation approach combining the two benefits. It is based on a superposition of virtual spring-damper systems to drive a humanoid robot’s movement. The method relies on a statistical description of the springs attractor points acting in different candidate frames of reference. It extends dynamic movement primitives models by formulating the dynamical systems parameters estimation problem as a Gaussian mixture regression problem with projection in different coordinate systems. The robot exploits local variability information extracted from multiple demonstrations of movements to determine which frames are relevant for the
task, and how the movement should be modulated with respect to these frames. The approach is tested on the new prototype of the COMAN compliant humanoid with time-based and timeinvariant movements, including bimanual coordination skills.
Original languageEnglish
Title of host publication2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Print)978-1-4673-1369-8
Publication statusPublished - 2012


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