Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning
Stella Kombo, Joel Burdick, Masih Haseli, Skylar X. Wei
AI summary
Problem
Autonomous robots must predict the motions of nearby dynamic agents from partial, noisy, and non-stationary sensor data to ensure safe navigation, but existing methods struggle with real-time adaptation under unknown noise distributions.
Approach
The method continuously embeds streaming measurements into a Hankel matrix, uses a Page matrix with singular value hard thresholding to automatically estimate the system's rank, applies Cadzow projection for denoising, and constructs a time-varying linear predictor for multi-step forecasting.
Key results
- Proved Page-Hankel rank equivalence for reliable data-driven rank estimation
- Developed an adaptive rank selection method that automatically tracks non-stationary dynamics
- Achieved stable, variance-aware denoising and accurate short-horizon predictions under Gaussian and heavy-tailed noise
- Validated real-time performance and robustness on a dynamic crane testbed
Why it matters
Enables safe, real-time robotic motion planning in dynamic environments by providing reliable, uncertainty-aware predictions from noisy sensor data without offline training.
Abstract
Autonomous systems often must predict the mo- tions 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 denoise the Hankel matrix and estimate its 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.