[2605.03260] Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
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Computer Science > Robotics
arXiv:2605.03260 (cs)
[Submitted on 5 May 2026]
Title:Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
Authors:Shugen Song, Wenjie Mei, Chengyan Zhao
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Abstract:Model Predictive Path Integral (MPPI) control is a powerful sampling-based strategy for nonlinear autonomous systems. However, its performance is often bottlenecked by the fidelity of nominal dynamics. We propose ICODE-MPPI, a robust framework that leverages Input Concomitant Neural Ordinary Differential Equations (ICODEs) to learn and compensate for unmodeled residual dynamics. Unlike discrete-time learners, ICODEs maintain physical consistency and temporal continuity during the MPPI prediction horizon. High-fidelity simulations on complex trajectories demonstrate that ICODE-MPPI achieves up to a 69\% reduction in cross-tracking error under persistent disturbances compared to standard MPPI control. Furthermore, our analysis confirms that ICODE-MPPI significantly suppresses control chattering, yielding smoother steering commands and superior robust performance.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.03260 [cs.RO]
(or arXiv:2605.03260v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.03260
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Wenjie Mei [view email] [v1] Tue, 5 May 2026 01:20:51 UTC (204 KB)
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