Learning-Based Distributionally Robust Motion Control with Gaussian Processes
Astghik Hakobyan,Insoon Yang,Astghik Hakobyan,Insoon Yang
Safety is a critical issue in learning-based robotic and autonomous systems as learned information about their environments is often unreliable and inaccurate. In this paper, we propose a risk-aware motion control tool that is robust against errors in learned distributional information about obstacles moving with unknown dynamics. The salient feature of our model predictive control (MPC) method is...
