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Gaussian path model library for intuitive robot motion programming by demonstration
September 12, 2025 Β· Declared Dead Β· π 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)
"No code URL or promise found in abstract"
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Authors
Samuli Soutukorva, Markku Suomalainen, Martin Kollingbaum, Tapio HeikkilΓ€
arXiv ID
2509.10007
Category
cs.RO: Robotics
Citations
0
Venue
2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)
Last Checked
4 months ago
Abstract
This paper presents a system for generating Gaussian path models from teaching data representing the path shape. In addition, methods for using these path models to classify human demonstrations of paths are introduced. By generating a library of multiple Gaussian path models of various shapes, human demonstrations can be used for intuitive robot motion programming. A method for modifying existing Gaussian path models by demonstration through geometric analysis is also presented.
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