GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments

2024-10-19

AI Summary

In localization-constrained environments, existing autonomous navigation frameworks fail to model the heterogeneous positional accuracy requirements across diverse tasks. To address this, we propose GUIDE—a context-aware uncertainty-adaptive navigation framework. Our method introduces (1) a Task-Specific Uncertainty Map (TSUM), the first unified representation that jointly encodes task semantics, environmental structure, and state estimation uncertainty; (2) a domain-adapted encoder, an uncertainty abstraction mapping, and a policy-conditioning mechanism, enabling end-to-end reinforcement learning without handcrafted uncertainty rewards; and (3) real-robot deployment demonstrating significant improvement in task success rate while automatically balancing localization accuracy against computational and resource overhead. GUIDE establishes a generalizable, uncertainty-aware paradigm for robust autonomous navigation under resource constraints.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Calibration & Uncertainty Quantification

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
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Autonomous vehicles performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. In many scenarios, such as stealth operations or resource-constrained settings, accessing high-precision localization comes at a significant cost, forcing robots to rely primarily on less precise state estimates. Our key observation is that different tasks require varying levels of precision in different regions: a robot navigating a crowded space might need precise localization near obstacles but can operate effectively with less precision elsewhere. In this paper, we present a planning method for integrating task-specific uncertainty requirements directly into navigation policies. We introduce Task-Specific Uncertainty Maps (TSUMs), which abstract the acceptable levels of state estimation uncertainty across different regions. TSUMs align task requirements and environmental features using a shared representation space, generated via a domain-adapted encoder. Using TSUMs, we propose Generalized Uncertainty Integration for Decision-Making and Execution (GUIDE), a policy conditioning framework that incorporates these uncertainty requirements into robot decision-making. We find that TSUMs provide an effective way to abstract task-specific uncertainty requirements, and conditioning policies on TSUMs enables the robot to reason about the context-dependent value of certainty and adapt its behavior accordingly. We show how integrating GUIDE into reinforcement learning frameworks allows the agent to learn navigation policies that effectively balance task completion and uncertainty management without explicit reward engineering. We evaluate GUIDE on various real-world robotic navigation tasks and find that it demonstrates significant improvement in task completion rates compared to baseline methods that do not explicitly consider task-specific uncertainty.

Problem

• Autonomous Navigation
• Position Uncertainty
• Task Success Rate

Innovation

• Task-Specific Uncertainty Maps
• GUIDE Framework
• Enhanced Navigation Accuracy