# Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements

arXiv: https://arxiv.org/abs/2505.13837 · HTML: https://mlsanity.com/paper/2505.13837/

## 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 (most cited first, 30 of 33)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Learning Latent Plans from Play](https://mlsanity.com/paper/1903.01973/) (2019)
- [Improving Sample Efficiency in Model-Free Reinforcement Learning from Images](https://mlsanity.com/paper/1910.01741/) (2019)
- [Automatic Goal Generation for Reinforcement Learning Agents](https://mlsanity.com/paper/1705.06366/) (2017)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Learning Exploration Policies for Navigation](https://mlsanity.com/paper/1903.01959/) (2019)
- [Learning to be Safe: Deep RL with a Safety Critic](https://mlsanity.com/paper/2010.14603/) (2020)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [GOAT: GO to Any Thing](https://mlsanity.com/paper/2311.06430/) (2023)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning](https://mlsanity.com/paper/1708.02190/) (2017)
- [Safe Reinforcement Learning with Model Uncertainty Estimates](https://mlsanity.com/paper/1810.08700/) (2018)
- [POPGym: Benchmarking Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2303.01859/) (2023)
- [Active Learning in Robotics: A Review of Control Principles](https://mlsanity.com/paper/2106.13697/) (2021)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness](https://mlsanity.com/paper/2403.10454/) (2024)
- [Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds](https://mlsanity.com/paper/2010.14838/) (2020)
- [Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning](https://mlsanity.com/paper/2010.10903/) (2020)
- [Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments](https://mlsanity.com/paper/1909.10972/) (2019)
- [Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction](https://mlsanity.com/paper/2104.07282/) (2021)
- [Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning](https://mlsanity.com/paper/2303.06710/) (2023)
- [Efficient Dependency-Guided Named Entity Recognition](https://mlsanity.com/paper/1810.08436/) (2018)
- [Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment](https://mlsanity.com/paper/2312.03263/) (2023)

## Built on by (most cited first, 2 of 2)

- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)
- [StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation](https://mlsanity.com/paper/2603.23571/) (2026)

## 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.