用贝叶斯方法零样本学习机器人动力学参数，无需大量实测数据。
Zero-Shot Parameter Learning of Robot Dynamics Using Bayesian Statistics and Prior Knowledge
- 基于贝叶斯框架融合先验知识，实现少样本甚至无测量下的参数识别。
- 成功学习了机械臂的惯性、机械与基座参数，且结果具物理合理性。
- 适用于无图纸或无数据手册的六轴机械臂，适合工业部署场景。
工业机器人惯性参数辨识是成熟技术，但传统最小二乘或机器学习方法不利用机器人先验信息，且需大量实测数据。受贝叶斯统计启发，本文提出一种新方法，具备更强泛化能力，可融合先验知识，在仅少量或无额外测量条件下完成参数学习（零样本学习）。该方法不仅能准确识别MABI Max 100机械臂的惯性、机械及基座参数，还确保结果符合物理规律，并给出参数置信区间。针对无数据手册或CAD模型的六自由度串联机械臂，我们提供了多种先验形式，适用于实际工程部署。
原文摘要 · Abstract (English)
Inertial parameter identification of industrial robots is an established process, but standard methods using Least Squares or Machine Learning do not consider prior information about the robot and require extensive measurements. Inspired by Bayesian statistics, this paper presents an identification method with improved generalization that incorporates prior knowledge and is able to learn with only a few or without additional measurements (Zero-Shot Learning). Furthermore, our method is able to correctly learn not only the inertial but also the mechanical and base parameters of the MABI Max 100 robot while ensuring physical feasibility and specifying the confidence intervals of the results. We also provide different types of priors for serial robots with 6 degrees of freedom, where datasheets or CAD models are not available.
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