Learning the residual dynamics of a delta robot : a sparse identification approach for rigid-body models
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2026
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Abstract
Accurate torque models are critical for high-performance model-based control of industrial robots, yet nominal rigid-body inverse dynamics often neglect friction and other nonlinear effects. This paper proposes a gray-box approach that identifies a sparse, interpretable residual torque model directly in torque space by regressing the mismatch between a nominal inverse-dynamics model and motor-current-based torque measurements collected from production-like pick-and-place motions of a parallel delta manipulator. The resulting correction improves agreement between predicted and measured torques on a fully held-out, unseen trajectory while retaining a compact structure with physically plausible velocity- and acceleration-dependent terms. A systematic hyperparameter study is conducted to quantify the sparsity-accuracy tradeoff and to select a model suitable for real-time model-based feedforward control.