Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements

TL;DR

This paper introduces GUIDE, a framework that improves robot navigation by integrating task-specific uncertainty management through TSUMs, enabling adaptive decision-making based on context and reducing the need for reward engineering.

Contribution

The paper proposes a novel framework, GUIDE, that incorporates task-specific uncertainty maps into navigation policies, enhancing adaptability and performance in complex environments.

Findings

GUIDE outperforms baseline methods in real-world tests.

Task-specific uncertainty management improves navigation accuracy.

Reinforcement learning with GUIDE reduces reward engineering complexity.

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.