Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements

• URL: http://arxiv.org/abs/2505.13837v1
• Date: Tue, 20 May 2025 02:23:15 GMT
• Title: Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
• Authors: Gokul Puthumanaillam, Paulo Padrao, Jose Fuentes, Leonardo Bobadilla, Melkior Ornik,
• Abstract summary: We present GUIDE, a framework that integrates task-specific requirements into navigation policies via Task-Specific Uncertainty Maps (TSUMs)<n>TSUMs assign acceptable uncertainty levels to different locations, enabling robots to adapt uncertainty management based on context.<n>Real-world tests show significant performance gains over methods lacking task-specific uncertainty awareness.
• Score: 1.614803913005309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots navigating complex environments must manage uncertainty from sensor noise, environmental changes, and incomplete information, with different tasks requiring varying levels of precision in different areas. For example, precise localization may be crucial near obstacles but less critical in open spaces. We present GUIDE (Generalized Uncertainty Integration for Decision-Making and Execution), a framework that integrates these task-specific requirements into navigation policies via Task-Specific Uncertainty Maps (TSUMs). By assigning acceptable uncertainty levels to different locations, TSUMs enable robots to adapt uncertainty management based on context. When combined with reinforcement learning, GUIDE learns policies that balance task completion and uncertainty management without extensive reward engineering. Real-world tests show significant performance gains over methods lacking task-specific uncertainty awareness.

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