Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning

• URL: http://arxiv.org/abs/2511.00814v1
• Date: Sun, 02 Nov 2025 05:54:30 GMT
• Title: Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning
• Authors: Stella Kombo, Masih Haseli, Skylar Wei, Joel W. Burdick,
• Abstract summary: We develop an online framework to learn, in real-time, a nonlinear predictive model of another agent's motions.<n>A Cadzow projection enforces structured low-rank consistency, yielding a denoised trajectory and local noise variance estimates.<n>We validate the approach in simulation under Gaussian and heavy-tailed noise, and experimentally on a dynamic crane testbed.
• Score: 7.819710421921815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous systems often must predict the motions of nearby agents from partial and noisy data. This paper asks and answers the question: "can we learn, in real-time, a nonlinear predictive model of another agent's motions?" Our online framework denoises and forecasts such dynamics using a modified sliding-window Hankel Dynamic Mode Decomposition (Hankel-DMD). Partial noisy measurements are embedded into a Hankel matrix, while an associated Page matrix enables singular-value hard thresholding (SVHT) to estimate the effective rank. A Cadzow projection enforces structured low-rank consistency, yielding a denoised trajectory and local noise variance estimates. From this representation, a time-varying Hankel-DMD lifted linear predictor is constructed for multi-step forecasts. The residual analysis provides variance-tracking signals that can support downstream estimators and risk-aware planning. We validate the approach in simulation under Gaussian and heavy-tailed noise, and experimentally on a dynamic crane testbed. Results show that the method achieves stable variance-aware denoising and short-horizon prediction suitable for integration into real-time control frameworks.

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