A Unified Formulation of Geometry-Aware Discrete Dynamic Movement Primitives

Abu-Dakka, Fares  J.; Saveriano, Matteo; Kyrki, Ville

doi:10.2139/ssrn.4559733

2023

DOI: 10.2139/ssrn.4559733

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A Unified Formulation of Geometry-Aware Discrete Dynamic Movement Primitives

Fares J. Abu-Dakka,

Matteo Saveriano,

Ville Kyrki

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A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Barekatain,

Habibi,

Voos

2024

Robotics

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This paper provides a structured and practical roadmap for practitioners to integrate learning from demonstration (LfD) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of “What to Demonstrate”, “How to Demonstrate”, “How to Learn”, and “How to Refine”. To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. This paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, this paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.

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A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Barekatain,

Habibi,

Voos

2024

Robotics

View full text Add to dashboard Cite

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A Unified Formulation of Geometry-Aware Discrete Dynamic Movement Primitives

Cited by 1 publication

References 15 publications

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

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