Gaussian path model library for intuitive robot motion programming by demonstration

Samuli Soutukorva; Markku Suomalainen; Martin Kollingbaum; Tapio Heikkilä

doi:10.1109/CASE58245.2025.11163890

Gaussian path model library for intuitive robot motion programming by demonstration

BA5602 Intelligent robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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.

Original language	English
Title of host publication	2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	2307-2314
Number of pages	8
ISBN (Electronic)	979-8-3315-2246-9
ISBN (Print)	979-8-3315-2247-6
DOIs	https://doi.org/10.1109/CASE58245.2025.11163890
Publication status	Published - Aug 2025
MoE publication type	A4 Article in a conference publication
Event	IEEE 21st International Conference on Automation Science and Engineering, CASE 2025 - Los Angeles, United States Duration: 17 Aug 2025 → 21 Aug 2025 Conference number: 21 https://ieeexplore.ieee.org/xpl/conhome/11163731/proceeding

Conference

Conference	IEEE 21st International Conference on Automation Science and Engineering, CASE 2025
Country/Territory	United States
City	Los Angeles
Period	17/08/25 → 21/08/25
Internet address	https://ieeexplore.ieee.org/xpl/conhome/11163731/proceeding

Funding

This work was supported by VTT Technical Research Centre of Finland and the INVERSE project, funded by the European Union, Horizon Europe research and innovation programme (Grant Agreement 101136067).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Programming by Demonstration
Learning and Adaptive Systems
Machine learning
Flexible Manufacturing

Access to Document

10.1109/CASE58245.2025.11163890

Cite this

@inproceedings{4e4f0d50f2e041af9cfa436ab048d3a9,

title = "Gaussian path model library for intuitive robot motion programming by demonstration",

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.",

keywords = "Programming by Demonstration, Learning and Adaptive Systems, Machine learning, Flexible Manufacturing",

author = "Samuli Soutukorva and Markku Suomalainen and Martin Kollingbaum and Tapio Heikkil{\"a}",

year = "2025",

month = aug,

doi = "10.1109/CASE58245.2025.11163890",

language = "English",

isbn = "979-8-3315-2247-6",

pages = "2307--2314",

booktitle = "2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "IEEE 21st International Conference on Automation Science and Engineering, CASE 2025 ; Conference date: 17-08-2025 Through 21-08-2025",

url = "https://ieeexplore.ieee.org/xpl/conhome/11163731/proceeding",

}

Soutukorva, S , Suomalainen, M, Kollingbaum, M & Heikkilä, T 2025, Gaussian path model library for intuitive robot motion programming by demonstration. in 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE). IEEE Institute of Electrical and Electronic Engineers, pp. 2307-2314, IEEE 21st International Conference on Automation Science and Engineering, CASE 2025, Los Angeles, California, United States, 17/08/25. https://doi.org/10.1109/CASE58245.2025.11163890

Gaussian path model library for intuitive robot motion programming by demonstration. / Soutukorva, Samuli ; Suomalainen, Markku; Kollingbaum, Martin et al.
2025 IEEE 21st International Conference on Automation Science and Engineering (CASE). IEEE Institute of Electrical and Electronic Engineers, 2025. p. 2307-2314.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Gaussian path model library for intuitive robot motion programming by demonstration

AU - Soutukorva, Samuli

AU - Suomalainen, Markku

AU - Kollingbaum, Martin

AU - Heikkilä, Tapio

N1 - Conference code: 21

PY - 2025/8

Y1 - 2025/8

N2 - 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.

AB - 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.

KW - Programming by Demonstration

KW - Learning and Adaptive Systems

KW - Machine learning

KW - Flexible Manufacturing

UR - https://www.scopus.com/pages/publications/105018301037

U2 - 10.1109/CASE58245.2025.11163890

DO - 10.1109/CASE58245.2025.11163890

M3 - Conference article in proceedings

SN - 979-8-3315-2247-6

SP - 2307

EP - 2314

BT - 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - IEEE 21st International Conference on Automation Science and Engineering, CASE 2025

Y2 - 17 August 2025 through 21 August 2025

ER -

Gaussian path model library for intuitive robot motion programming by demonstration

Abstract

Conference

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this