Safeguarding Learning-Based Planners Under Motion and Sensing Uncertainties Using Reachability Analysis

Abstract

Learning-based trajectory planners in robotics have attracted growing interest given their ability to plan for complex tasks. These planners are typically trained in simulation under nominal conditions before being implemented on real robots. However, in real settings, the presence of motion and sensing uncertainties causes the robot to deviate from planned reference trajectories potentially leading to unsafe outcomes such as collisions. In this paper we present a reachability analysis to predict such deviations and to evaluate robot safety along reference trajectories. We then use the reachability analysis to safeguard a learning-based planner. Finally, we demonstrate the applicability of our safeguarding algorithm for learning-based planners via multiple simulations and real robot experiments.