用图神经网络学习可解释的逆运动学模型，支持不同机械臂配置。
The Road to Learning Explainable Inverse Kinematic Models: Graph Neural Networks as Inductive Bias for Symbolic Regression
- 用自动生成数据训练GNN，实现3-5自由度机械臂逆运动学建模
- 3自由度位置误差<1.0厘米，6自由度姿态误差8.2度，满足实际应用
- 结果可作为符号回归的先验知识，助力生成可解释解析公式
本文展示如何利用图神经网络（GNN）基于自动生成的数据学习逆运动学（IK）模型。该模型可泛化至具有相同自由度（DOF）但连杆长度配置不同的机械臂家族。实验结果表明，3自由度系统的位姿误差小于1.0厘米，5自由度系统为4.5厘米；姿态误差方面，3自由度为2°，6自由度为8.2°，具备部署到部分真实场景的潜力。然而，模型在域外样本上出现误差且缺乏外推能力。对这些误差的深入分析揭示了未来改进空间。因此，所生成的GNN将被用于后续工作，作为符号回归的归纳偏置，以推导出解析表达式。
原文摘要 · Abstract (English)
This paper shows how a Graph Neural Network (GNN) can be used to learn an Inverse Kinematics (IK) based on an automatically generated dataset. The generated Inverse Kinematics is generalized to a family of manipulators with the same Degree of Freedom (DOF), but varying link length configurations. The results indicate a position error of less than 1.0 cm for 3 DOF and 4.5 cm for 5 DOF, and orientation error of 2$^\circ$ for 3 DOF and 8.2$^\circ$ for 6 DOF, which allows the deployment to certain real world-problems. However, out-of-domain errors and lack of extrapolation can be observed in the resulting GNN. An extensive analysis of these errors indicates potential for enhancement in the future. Consequently, the generated GNNs are tailored to be used in future work as an inductive bias to generate analytical equations through symbolic regression.
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