GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics

Understand

Gaussian Processes (GPs) are expressive models for capturing signal statistics and expressing prediction uncertainty.

• As a result, the robotics community has gathered interest in leveraging these methods for inference, planning, and control.
• Unfortunately, despite providing a closed-form inference solution, GPs are non-parametric models that typically scale cubically with the dataset size, hence making them difficult to be used especially on onboard Size, Weight, and Power (SWaP) constrained aerial robots.
• In addition, the integration of popular libraries with GPs for different kernels is not trivial.