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 [BibTeX] [Marc21]
Gaussians on Riemannian Manifolds for Robot Learning and Adaptive Control
Type of publication: Journal paper
Citation: Calinon_RAM_2020
Publication status: Accepted
Journal: IEEE Robotics and Automation Magazine (RAM)
Year: 2020
Abstract: This article presents an overview of robot learning and adaptive control applications that can benefit from a joint use of Riemannian geometry and probabilistic representations. The roles of Riemannian manifolds, geodesics and parallel transport in robotics are first discussed. Several forms of manifolds already employed in robotics are then presented, by also listing manifolds that have been underexploited but that have potentials in future robot learning applications. A varied range of techniques employing Gaussian distributions on Riemannian manifolds are then introduced, including clustering, regression, information fusion, planning and control problems. Two examples of applications are presented, involving the control of a prosthetic hand from surface electromyography (sEMG) data, and the teleoperation of a bimanual underwater robot. Further perspectives are finally discussed, with suggestions of promising research directions.
Keywords: Riemannian geometry, robot learning
Projects Idiap
Authors Calinon, Sylvain
Added by: [UNK]
Total mark: 0
  • Calinon_RAM_2020.pdf