mlsanityData to 14 Oct 2026

Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements

arXiv 2505.13837 ↗First posted 20 May 2025cs.RO cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 71 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

33 of 71 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensingcited by 1
  2. 2026StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments
  2. 2026Toward Integrating Adaptive Experience Replay and Online Uncertainty Estimation in Safe Actor-Critic Optimal Control
  3. 2026GroundControl: Anticipating Navigation Failures in Vision-Language Agents via Trajectory-Consistent Uncertainty Estimates
  4. 2024Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning
  5. 2026When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
  6. 2026Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation
  7. 2025Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
  8. 2026NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation