ProSIP: Probabilistic Surface Interaction Primitives for Learning of Robotic Cleaning of Edges

Christoph Unger, Christian Hartl-Nesic, Minh Nhat Vu, Andreas Kugi

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung

Abstract

Learning from demonstration (LfD) has emerged as a promising approach enabling robots to acquire complex tasks directly from human demonstrations. However, tasks involving surface interactions on freeform 3D surfaces present unique challenges in modeling and execution, especially when geometric variations exist between demonstrations and robot execution. This paper proposes a novel framework called probabilistic surface interaction primitives (ProSIP), which systematically incorporates the surface path and the local surface features into the learning procedure. An instrumented tool allows seamless recording and execution of human demonstrations. By design, ProSIPs are independent of time, invariant to rigid-body displacements, and apply to any robotic platform with a Cartesian controller. The framework is employed for an edge-cleaning task of bathroom sinks. The generalization capability to various object geometries and significantly distorted objects is demonstrated. Simulations and an experimental setup with a 9-degrees-of-freedom robotic platform confirm the performance.
OriginalspracheEnglisch
TitelProceedings of the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Seiten5956-5963
DOIs
PublikationsstatusVeröffentlicht - 25 Dez. 2024
Veranstaltung2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Abu Dhabi, Vereinigte Arabische Emirate
Dauer: 14 Okt. 202418 Okt. 2024

Konferenz

Konferenz2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Land/GebietVereinigte Arabische Emirate
StadtAbu Dhabi
Zeitraum14/10/2418/10/24

Research Field

  • Complex Dynamical Systems

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  • IROS Best Application Paper Award

    Unger, C. (Empfänger/-in), Hartl-Nesic, C. (Empfänger/-in), Vu, M. N. (Empfänger/-in) & Kugi, A. (Empfänger/-in), 17 Okt. 2024

    Auszeichnung: Best paper award für Beitrag in einer Fachzeitschrift/ auf einer Konferenz

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