mlsanityData to 14 Oct 2026

NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv 2503.02111 ↗First posted 3 Mar 2025cs.ROcited by 0

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

Abstract (arXiv)

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points--a novel directional cue within a reinforcement learning-based framework--is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot's ability to confidently navigate around obstacles and robustly avoid pedestrians.

Key facts. No citing papers in the map yet (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 23 of its 36 references that are in the map. First posted on arXiv 3 Mar 2025.

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23 of 36 references.

References · 23

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019Object Detection in 20 Years: A Surveycited by 531
  3. 2023NoMaD: Goal Masked Diffusion Policies for Navigation and Explorationcited by 248
  4. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 175
  5. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 118
  6. 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 111
  7. 2024Agile But Safe: Learning Collision-Free High-Speed Legged Locomotioncited by 73
  8. 2024EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Modelcited by 65
  9. 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
  10. 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 49
  11. 2023ViPlanner: Visual Semantic Imperative Learning for Local Navigationcited by 46
  12. 2023DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstaclescited by 37
11 more
  1. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
  2. 2023EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomycited by 21
  3. 2021APPL: Adaptive Planner Parameter Learningcited by 20
  4. 2021TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learningcited by 20
  5. 2022Risk-Aware Off-Road Navigation via a Learned Speed Distribution Mapcited by 20
  6. 2021Conflict Avoidance in Social Navigation -- a Surveycited by 14
  7. 2022Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planningcited by 14
  8. 2023Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigationcited by 5
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  10. 2013Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixturecited by 2
  11. 2020LAMP: Learning a Motion Policy to Repeatedly Navigate in an Uncertain Environmentcited by 2

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  1. 2024Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control
  2. 2024Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots
  3. 2026DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
  4. 2026SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments
  5. 2025Adaptive Dynamics Planning for Robot Navigation
  6. 2022A Closed-Loop Perception, Decision-Making and Reasoning Mechanism for Human-Like Navigation
  7. 2024Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning
  8. 2026DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions