{"ID":2864727,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23312","arxiv_id":"2509.23312","title":"GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems","abstract":"This paper presents the framework \\textbf{GUARD} (\\textbf{G}uided robot control via \\textbf{U}ncertainty attribution and prob\\textbf{A}bilistic kernel optimization for \\textbf{R}isk-aware \\textbf{D}ecision 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, \\emph{GUARD} inherently handles the existing ambiguity of the term \\textit{safety} that exists in robotics literature. Experimental studies indicate the high performance of \\emph{GUARD}, thereby highlighting the relevance and need to broaden its applicability in future.","short_abstract":"This paper presents the framework \\textbf{GUARD} (\\textbf{G}uided robot control via \\textbf{U}ncertainty attribution and prob\\textbf{A}bilistic kernel optimization for \\textbf{R}isk-aware \\textbf{D}ecision making) that combines traditional control with an uncertainty-aware perception technique using active learning wit...","url_abs":"https://arxiv.org/abs/2509.23312","url_pdf":"https://arxiv.org/pdf/2509.23312v1","authors":"[\"Johannes A. Gaus\",\"Junheon Yoon\",\"Woo-Jeong Baek\",\"Seungwon Choi\",\"Suhan Park\",\"Jaeheung Park\"]","published":"2025-09-27T13:59:43Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}