![[2602.12487] Gradient-Enhanced Partitioned Gaussian Processes for Real-Time Quadrotor Dynamics Modeling](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.12487] Gradient-Enhanced Partitioned Gaussian Processes for Real-Time Quadrotor Dynamics Modeling
Summary
This paper introduces a novel Gaussian Process model for quadrotor dynamics that integrates gradient information, enabling real-time inference and improved accuracy while reducing computational costs.
Why It Matters
The research addresses the challenge of real-time simulations in quadrotor dynamics by enhancing traditional Gaussian Process methods. This advancement is crucial for applications in robotics and autonomous systems, where quick and reliable predictions are essential for effective control in dynamic environments.
Key Takeaways
- Introduces a gradient-enhanced Gaussian Process for quadrotor dynamics.
- Achieves real-time inference at frequencies above 30 Hz on standard hardware.
- Utilizes state-space partitioning to reduce computational costs.
- Demonstrates improved accuracy over traditional partitioned GPs.
- Incorporates aerodynamic effects using mid-fidelity simulations.
Computer Science > Robotics arXiv:2602.12487 (cs) [Submitted on 13 Feb 2026] Title:Gradient-Enhanced Partitioned Gaussian Processes for Real-Time Quadrotor Dynamics Modeling Authors:Xinhuan Sang, Adam Rozman, Sheryl Grace, Roberto Tron View a PDF of the paper titled Gradient-Enhanced Partitioned Gaussian Processes for Real-Time Quadrotor Dynamics Modeling, by Xinhuan Sang and 2 other authors View PDF HTML (experimental) Abstract:We present a quadrotor dynamics Gaussian Process (GP) with gradient information that achieves real-time inference via state-space partitioning and approximation, and that includes aerodynamic effects using data from mid-fidelity potential flow simulations. While traditional GP-based approaches provide reliable Bayesian predictions with uncertainty quantification, they are computationally expensive and thus unsuitable for real-time simulations. To address this challenge, we integrate gradient information to improve accuracy and introduce a novel partitioning and approximation strategy to reduce online computational cost. In particular, for the latter, we associate a local GP with each non-overlapping region; by splitting the training data into local near and far subsets, and by using Schur complements, we show that a large part of the matrix inversions required for inference can be performed offline, enabling real-time inference at frequencies above 30 Hz on standard desktop hardware. To generate a training dataset that captures aerodynamic effects,...
