优化人示范时的力信号，让机器人更准确理解操作意图。
Optimizing Force Signals from Human Demonstrations of In-Contact Motions
- 用峰值检测法修正接触瞬间的信号偏差。
- 可将动作误差降低最多20%。
- 适合非专业用户进行机器人编程。
对于非机器人编程专家而言，通过身体引导进行机器人操作是一种直观的输入方式，尤其在需要接触的任务中越来越重要。然而，人类示范产生的力信号常存在不精确和噪声问题，直接影响运动复现或作为机器学习输入的效果。本文研究如何优化力信号，使其更贴近人类示范的真实意图。比较了多种信号滤波方法，并提出一种针对首次接触偏差的峰值检测方法。评估基于专门设计的输入与人类意图间误差指标。同时分析了关键参数对滤波效果的影响。实验表明，单个动作的信号质量可提升最多20%。该方法有助于提高机器人编程的易用性与人机交互体验。
原文摘要 · Abstract (English)
For non-robot-programming experts, kinesthetic guiding can be an intuitive input method, as robot programming of in-contact tasks is becoming more prominent. However, imprecise and noisy input signals from human demonstrations pose problems when reproducing motions directly or using the signal as input for machine learning methods. This paper explores optimizing force signals to correspond better to the human intention of the demonstrated signal. We compare different signal filtering methods and propose a peak detection method for dealing with first-contact deviations in the signal. The evaluation of these methods considers a specialized error criterion between the input and the human-intended signal. In addition, we analyze the critical parameters' influence on the filtering methods. The quality for an individual motion could be increased by up to \SI{20}{\percent} concerning the error criterion. The proposed contribution can improve the usability of robot programming and the interaction between humans and robots.
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