Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913999013019648 |
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| author | Duan, Zebin Hagelskjær, Frederik Kramberger, Aljaz Heredia, Juan Krüger, Norbert |
| author_facet | Duan, Zebin Hagelskjær, Frederik Kramberger, Aljaz Heredia, Juan Krüger, Norbert |
| contents | The automation of robotic tasks requires high precision and adaptability, particularly in force-based operations such as insertions. Traditional learning-based approaches either rely on static datasets, which limit their ability to generalize, or require frequent manual intervention to maintain good performances. As a result, ensuring long-term reliability without human supervision remains a significant challenge. To address this, we propose an adaptive self-supervised learning framework for insertion classification that continuously improves its precision over time. The framework operates in real-time, incrementally refining its classification decisions by integrating newly acquired force data. Unlike conventional methods, it does not rely on pre-collected datasets but instead evolves dynamically with each task execution. Through real-world experiments, we demonstrate how the system progressively reduces execution time while maintaining near-perfect precision as more samples are processed. This adaptability ensures long-term reliability in force-based robotic tasks while minimizing the need for manual intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification Duan, Zebin Hagelskjær, Frederik Kramberger, Aljaz Heredia, Juan Krüger, Norbert Robotics The automation of robotic tasks requires high precision and adaptability, particularly in force-based operations such as insertions. Traditional learning-based approaches either rely on static datasets, which limit their ability to generalize, or require frequent manual intervention to maintain good performances. As a result, ensuring long-term reliability without human supervision remains a significant challenge. To address this, we propose an adaptive self-supervised learning framework for insertion classification that continuously improves its precision over time. The framework operates in real-time, incrementally refining its classification decisions by integrating newly acquired force data. Unlike conventional methods, it does not rely on pre-collected datasets but instead evolves dynamically with each task execution. Through real-world experiments, we demonstrate how the system progressively reduces execution time while maintaining near-perfect precision as more samples are processed. This adaptability ensures long-term reliability in force-based robotic tasks while minimizing the need for manual intervention. |
| title | Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.02153 |