Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
This work addresses uncertainty management in robot navigation for robotics applications, representing an incremental improvement by combining existing concepts like uncertainty maps with reinforcement learning.
The paper tackles the problem of robots managing uncertainty in navigation by introducing GUIDE, a framework that integrates task-specific uncertainty maps to adapt uncertainty management based on context, resulting in significant performance gains in real-world tests.
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.