Appendix for the Paper : Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Agile Autonomous Driving using End-to-End Deep Imitation Learning (2017, methodology): “We use an MPC expert [22] based on an incremental Sparse Spectrum Gaussian Process (SSGP) [15] dynamics model (which was learned from 30 minute-long driving data) and an iSAM2 state estimator [9].”
Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning (2017, methodology): “We use an MPC expert [21] based on an incremental Sparse Spectrum Gaussian Process (SSGP) dynamics model (which was learned from 30 minute-long driving data) and an iSAM2 state estimator [22].”
Learning-Based Model Predictive Control: Toward Safe Learning in Control (2020, background): “…of GP-based MPC to robotic systems have been presented, e.g., for trajectory tracking with robotic manipulators (95), path following of off-road mobile robots (87, 96), autonomous racing (86, 90, 97), process control (98), and telescope systems in the context of periodic additive disturbances (99).”
Cautious Model Predictive Control Using Gaussian Process Regression (2017, methodology): “An application to high-performance driving using a sparse spectrum approximation of the GP was shown in [6] and constrained tracking MPC for robotic applications in [7] and [8], where the uncertainty of the GP prediction is taken into account using confidence bounds.”