mlsanityData to 14 Oct 2026

Perturbation-mitigated USV Navigation with Distributionally Robust Reinforcement Learning

arXiv 2512.00030 ↗First posted 8 Nov 2025cs.RO cs.AIcited by 1

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

The robustness of Unmanned Surface Vehicles (USV) is crucial when facing unknown and complex marine environments, especially when heteroscedastic observational noise poses significant challenges to sensor-based navigation tasks. Recently, Distributional Reinforcement Learning (DistRL) has shown promising results in some challenging autonomous navigation tasks without prior environmental information. However, these methods overlook situations where noise patterns vary across different environmental conditions, hindering safe navigation and disrupting the learning of value functions. To address the problem, we propose DRIQN to integrate Distributionally Robust Optimization (DRO) with implicit quantile networks to optimize worst-case performance under natural environmental conditions. Leveraging explicit subgroup modeling in the replay buffer, DRIQN incorporates heterogeneous noise sources and target robustness-critical scenarios. Experimental results based on the risk-sensitive environment demonstrate that DRIQN significantly outperforms state-of-the-art methods, achieving +13.51% success rate, -12.28% collision rate and +35.46% for time saving, +27.99% for energy saving, compared with the runner-up.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 34 references that are in the map. First posted on arXiv 8 Nov 2025.

Builds on

18 of 34 references.

References · 18

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  2. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  3. 2022Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexitycited by 87
  4. 2019Distributionally Robust Optimization and Generalization in Kernel Methodscited by 73
  5. 2018Safe Reinforcement Learning with Model Uncertainty Estimatescited by 72
  6. 2023The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Modelcited by 52
  7. 2020An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectivescited by 42
  8. 2023Robust Quadrupedal Locomotion via Risk-Averse Policy Learningcited by 19
  9. 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spacescited by 17
  10. 2018A Comparative Study of Bug Algorithms for Robot Navigationcited by 8
  11. 2023Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learningcited by 8
  12. 2025Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interactioncited by 7
6 more
  1. 2022Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learningcited by 6
  2. 2025HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agentscited by 6
  3. 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Taskscited by 5
  4. 2024Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vesselscited by 2
  5. 2025Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Datacited by 1
  6. 2025TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Quantile Geometry Regularization for Distributional Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles
  2. 2026Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation
  3. 2025Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
  4. 2024Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning
  5. 2025Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks
  6. 2024Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning
  7. 2025ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning
  8. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation