GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems

Abstract-- This paper presents the framework GUARD (Guided robot control via Uncertainty attribution and probAbilistic kernel optimization for Risk-aware Decision making) that combines traditional control with an uncertainty-aware perception technique using active learning with real-time capability for safe robot collision avoidance. By doing so, this manuscript addresses the central challenge in robotics of finding a reasonable compromise between traditional methods and learning algorithms to foster the development of safe, yet efficient and flexible applications. By unifying a reactive model predictive countouring control (RMPCC) with an Iterative Closest Point (ICP) algorithm that enables the attribution of uncertainty sources online using active learning with real-time capability via a probabilistic kernel optimization technique, GUARD inherently handles the existing ambiguity of the term safety that exists in robotics literature. Experimental studies indicate the high performance of GUARD, thereby highlighting the relevance and need to broaden its applicability in future. Developing safe and flexible robot applications is a central, yet open issue in robotics.