Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification

TL;DR

This paper introduces an adaptive self-supervised learning framework for force-based robotic insertion tasks that continuously improves precision over time without relying on static datasets, enhancing long-term reliability.

Contribution

The proposed framework dynamically evolves during task execution, reducing manual intervention and improving accuracy through real-time force data integration.

Findings

Progressively reduces execution time with more data

Maintains near-perfect precision during operation

Operates effectively in real-world robotic insertions

Abstract

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.