自适应自监督学习让机器人插入任务越做越准，无需人工干预。
Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification
- 实时融合新力觉数据，动态优化分类决策
- 处理样本越多，执行越快，精度接近完美
- 适合长期运行的力控机器人任务
机器人自动化任务需高精度与强适应性，尤其在基于力的插入操作中。传统学习方法依赖静态数据集，泛化能力有限，或需频繁人工干预以维持性能，导致长期无监督可靠性难以保障。为此，我们提出一种用于插入分类的自适应自监督学习框架，可随任务执行持续提升精度。该框架实时运行，通过增量式整合新获取的力觉数据，逐步优化分类判断。不同于传统方法，它不依赖预采集数据集，而是随每次任务动态演进。真实实验表明，系统在处理更多样本后，执行时间逐步减少，同时保持近乎完美的精度。该自适应机制确保了力控任务的长期可靠性，并显著降低人工介入需求。
原文摘要 · Abstract (English)
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.
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