Achieving Safe Control Online through Integration of Harmonic Control Lyapunov-Barrier Functions with Unsafe Object-Centric Action Policies

Open-world environments pose many challenges for autonomous robots as unexpected events or task modulations can make learned robot behavior inapplicable or obsolete. Consider, for example, a robot that has learned to autonomously perform a sorting task on a table top without any human interventions when a human co-worker steps in to help with finishing the task. This change in task environment now requires the robot to avoid colliding with the human whose arms are extended into the robot's work space and are dynamically changing position. Even if the robot has the perceptual capability to detect and track the human's arms and hands, its trained action policy does not provide a way to account for the motion constraints they impose. Or consider a delivery robot in a warehouse that has an optimized policy for traversing indoor spaces when dynamic constraints are imposed on where it can drive (e.g., because parts of the floor are painted).