Enhancing Robot Navigation Policies with Task-Specific Uncertainty Management

2025-05-20

arXiv.org

AI Summary

Robots navigating complex environments face uncertainty arising from sensor noise, environmental dynamics, and incomplete information; moreover, localization accuracy requirements vary significantly across task regions—e.g., high precision near obstacles versus relaxed tolerance in open areas. To address this, we propose the Task-Specific Uncertainty Map (TSUM) and the Generalized Uncertainty Integration for Decision-making and Estimation (GUIDE) framework. GUIDE is the first to explicitly model and embed spatially and task-coupled uncertainty tolerances into navigation policies, eliminating reliance on handcrafted reward functions. Our approach jointly models multi-sensor noise, performs online uncertainty propagation estimation, and integrates with model-free reinforcement learning (PPO/SAC). Real-world experiments demonstrate a 37% increase in path success rate, a 52% reduction in collision rate, and a 2.1× improvement in localization accuracy within uncertainty-sensitive regions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Managing uncertainty in robot navigation for complex environments
• Adapting uncertainty levels based on task-specific requirements
• Balancing task completion and uncertainty management efficiently

Innovation

• Task-Specific Uncertainty Maps (TSUMs) for navigation
• GUIDE framework integrates uncertainty into policies
• Reinforcement learning balances task and uncertainty