用符号回归让机器人动力学模型更可解释且更准。
Data-driven Interpretable Hybrid Robot Dynamics
- 用符号回归和SINDy从数据中提取简洁的残差项公式。
- 仿真中误差极小，泛化能力优于神经网络。
- 适合需要物理可解释性的机器人控制研究者。
我们研究数据驱动的可解释混合机器人动力学建模，将解析的刚体动力学模型与学习得到的残差力矩项结合。通过符号回归和非线性动力学稀疏识别（SINDy），从关节空间数据中恢复出该残差的紧凑闭式表达式。在已知动力学的7自由度Franka机械臂仿真中，这些可解释模型准确恢复了惯性、科里奥利、重力和粘滞效应，相对误差极小，且在精度和泛化性上均优于神经网络基线。在真实7自由度WAM机械臂数据上，符号回归残差显著优于SINDy和神经网络，后者易过拟合；符号回归还提出了扩展该机器人名义动力学模型的新闭式形式候选。总体表明，可解释的残差动力学模型为力矩预测提供了紧凑、准确且物理解释性强的黑箱替代方案。
原文摘要 · Abstract (English)
We study data-driven identification of interpretable hybrid robot dynamics, where an analytical rigid-body dynamics model is complemented by a learned residual torque term. Using symbolic regression and sparse identification of nonlinear dynamics (SINDy), we recover compact closed-form expressions for this residual from joint-space data. In simulation on a 7-DoF Franka arm with known dynamics, these interpretable models accurately recover inertial, Coriolis, gravity, and viscous effects with very small relative error and outperform neural-network baselines in both accuracy and generalization. On real data from a 7-DoF WAM arm, symbolic-regression residuals generalize substantially better than SINDy and neural networks, which tend to overfit, and suggest candidate new closed-form formulations that extend the nominal dynamics model for this robot. Overall, the results indicate that interpretable residual dynamics models provide compact, accurate, and physically meaningful alternatives to black-box function approximators for torque prediction.
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