Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions

TL;DR

This paper introduces a method for robots to safely learn and adapt to multiple changing operating conditions using an extended Gaussian process-based controller, enabling high performance without prior environment specifications.

Contribution

The authors extend a GP-based safe learning controller to handle multiple non-linear models, allowing safe adaptation to diverse and previously unseen conditions without extra computation.

Findings

Successfully adapted to various changing conditions in experiments

Enabled safe learning without prior environment knowledge

Maintained real-time control performance

Abstract

Learning has propelled the cutting edge of performance in robotic control to new heights, allowing robots to operate with high performance in conditions that were previously unimaginable. The majority of the work, however, assumes that the unknown parts are static or slowly changing. This limits them to static or slowly changing environments. However, in the real world, a robot may experience various unknown conditions. This paper presents a method to extend an existing single mode GP-based safe learning controller to learn an increasing number of non-linear models for the robot dynamics. We show that this approach enables a robot to re-use past experience from a large number of previously visited operating conditions, and to safely adapt when a new and distinct operating condition is encountered. This allows the robot to achieve safety and high performance in an large number of operating conditions that do not have to be specified ahead of time. Our approach runs independently from the controller, imposing no additional computation time on the control loop regardless of the number of previous operating conditions considered. We demonstrate the effectiveness of our approach in experiment on a 900\,kg ground robot with both physical and artificial changes to its dynamics. All of our experiments are conducted using vision for localization.