Data-driven Interpretable Hybrid Robot Dynamics

• URL: http://arxiv.org/abs/2512.11900v1
• Date: Wed, 10 Dec 2025 13:33:03 GMT
• Title: Data-driven Interpretable Hybrid Robot Dynamics
• Abstract summary: We study interpretable hybrid robot dynamics, where an analytical rigid-body dynamics model is complemented by a learned residual torque term.<n>Using symbolic regression and sparse identification of nonlinear dynamics (SINDy), we recover compact closed-form expressions for this residual from joint-space data.<n>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.
• Score: 13.097833648875396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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