Data-Efficient Constrained Robot Learning with Probabilistic Lagrangian Control

Abstract

We propose a novel framework for data-efficient black-box robot learning under constraints. Our approach integrates probabilistic inference with Lagrangian optimization. With the guide of a learned Gaussian process model, the Lagrange multiplier is controlled by the probability of whether the constraints would be satisfied. This reduces the typical oscillations seen in primal-dual updates and therefore improves both data efficiency and safety during learning. Both synthetic results and robot experiments demonstrate that our method is a scalable and effective solution for constrained robot learning problems.