mlsanityData to 14 Oct 2026

Learning Navigation Behaviors End-to-End with AutoRL

arXiv 1809.10124 ↗First posted 26 Sep 2018cs.RO cs.AI cs.LGcited by 70

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Abstract (arXiv)

We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles. These policies receive noisy lidar observations and output robot linear and angular velocities. The policies are trained in small, static environments with AutoRL, an evolutionary automation layer around Reinforcement Learning (RL) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization. AutoRL first finds a reward that maximizes task completion, and then finds a neural network architecture that maximizes the cumulative of the found reward. Empirical evaluations, both in simulation and on-robot, show that AutoRL policies do not suffer from the catastrophic forgetfulness that plagues many other deep reinforcement learning algorithms, generalize to new environments and moving obstacles, are robust to sensor, actuator, and localization noise, and can serve as robust building blocks for larger navigation tasks. Our path-following and point-to-point policies are respectively 23% and 26% more successful than comparison methods across new environments. Video at: https://youtu.be/0UwkjpUEcbI

Key facts. Cited by 70 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Sep 2018.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 19; 2022: 17; 2023: 16; 2024: 7; 2025: 6; 2026: 3.

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  1. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 571
  2. 2023Language to Rewards for Robotic Skill Synthesiscited by 312
  3. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
  4. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  5. 2021A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Executioncited by 91
  6. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 70
  7. 2023Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraftcited by 61
  8. 2021On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learningcited by 59
  9. 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
  10. 2021Coupling Vision and Proprioception for Navigation of Legged Robotscited by 51
  11. 2021Learning a State Representation and Navigation in Cluttered and Dynamic Environmentscited by 41
  12. 2022MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environmentscited by 40
38 more
  1. 2023Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Roboticscited by 36
  2. 2022Verifying Learning-Based Robotic Navigation Systemscited by 33
  3. 2022WayFAST: Navigation with Predictive Traversability in the Fieldcited by 32
  4. 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigationcited by 29
  5. 2023Evolutionary Reinforcement Learning: A Surveycited by 29
  6. 2022An information-theoretic perspective on intrinsic motivation in reinforcement learning: a surveycited by 28
  7. 2021Self-Imitation Learning by Planningcited by 26
  8. 2022VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamicscited by 21
  9. 2022Benchmarking Reinforcement Learning Techniques for Autonomous Navigationcited by 19
  10. 2021Visual Representation Learning for Preference-Aware Path Planningcited by 18
  11. 2022Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimizationcited by 18
  12. 2024Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environmentscited by 17
  13. 2021Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigationcited by 15
  14. 2021Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systemscited by 15
  15. 2021Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillationcited by 15
  16. 2020Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learningcited by 14
  17. 2022Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualizationcited by 14
  18. 2019Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigationcited by 13
  19. 2024Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splattingcited by 12
  20. 2022High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimizationcited by 11
  21. 2022Automated Reinforcement Learning: An Overviewcited by 9
  22. 2023Safe Deep Reinforcement Learning by Verifying Task-Level Propertiescited by 9
  23. 2021Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRLcited by 8
  24. 2024Generating and Evolving Reward Functions for Highway Driving with Large Language Modelscited by 8
  25. 2024Analyzing Adversarial Inputs in Deep Reinforcement Learningcited by 7
  26. 2025Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learningcited by 7
  27. 2021Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generatorscited by 6
  28. 2021Language Understanding for Field and Service Robots in a Priori Unknown Environmentscited by 6
  29. 2021Adaptively Calibrated Critic Estimates for Deep Reinforcement Learningcited by 6
  30. 2022Model-free Neural Lyapunov Control for Safe Robot Navigationcited by 6
  31. 2022Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specificationscited by 6
  32. 2025Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learningcited by 6
  33. 2021Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigationcited by 5
  34. 2022Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learningcited by 5
  35. 2025RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Rewardcited by 5
  36. 2021Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inferencecited by 4
  37. 2023Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learningcited by 4
  38. 2021Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robotscited by 3

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