mlsanityData to 7 Oct 2026

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

arXiv 1703.00420 ↗First posted 1 Mar 2017cs.RO cs.AI cs.LGcited by 90

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Abstract (arXiv)

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

Key facts. Cited by 90 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 29; 2022: 20; 2023: 15; 2024: 11; 2025: 9; 2026: 6.

90 papers.

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  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  2. 2021Memory-based Deep Reinforcement Learning for POMDPscited by 53
  3. 2022MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environmentscited by 40
  4. 2022Verifying Learning-Based Robotic Navigation Systemscited by 33
  5. 2023Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotioncited by 33
  6. 2025DiWA: Diffusion Policy Adaptation with World Modelscited by 28
  7. 2021Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehiclescited by 27
  8. 2024M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenescited by 26
  9. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
  10. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 23
  11. 2022Safe Reinforcement Learning Using Black-Box Reachability Analysiscited by 22
  12. 2025The Reality Gap in Robotics: Challenges, Solutions, and Best Practicescited by 22
38 more
  1. 2022VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamicscited by 19
  2. 2023Causal Policy Gradient for Whole-Body Mobile Manipulationcited by 18
  3. 2022State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersectionscited by 15
  4. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networkscited by 13
  5. 2024Verification-Guided Shielding for Deep Reinforcement Learningcited by 13
  6. 2021Curriculum Learning for Safe Mapless Navigationcited by 11
  7. 2022Constrained Reinforcement Learning for Robotics via Scenario-Based Programmingcited by 11
  8. 2021Training a Resilient Q-Network against Observational Interferencecited by 9
  9. 2021Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximationcited by 9
  10. 2021Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spacescited by 9
  11. 2022High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimizationcited by 9
  12. 2023Safe Deep Reinforcement Learning by Verifying Task-Level Propertiescited by 9
  13. 2022Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shapingcited by 8
  14. 2024ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Roboticscited by 8
  15. 2023Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigationcited by 7
  16. 2024Analyzing Adversarial Inputs in Deep Reinforcement Learningcited by 7
  17. 2021Language Understanding for Field and Service Robots in a Priori Unknown Environmentscited by 6
  18. 2021Towards Disturbance-Free Visual Mobile Manipulationcited by 6
  19. 2022Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigationcited by 6
  20. 2023Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learningcited by 6
  21. 2023Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learningcited by 6
  22. 2022Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learningcited by 5
  23. 2023Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learningcited by 5
  24. 2023PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learningcited by 5
  25. 2024TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environmentscited by 5
  26. 2021Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learningcited by 4
  27. 2021IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learningcited by 4
  28. 2021Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inferencecited by 4
  29. 2022Learning Continuous Control Policies for Information-Theoretic Active Perceptioncited by 4
  30. 2021Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Carscited by 3
  31. 2021VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mappingcited by 3
  32. 2022Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogisticscited by 3
  33. 2024Taming "data-hungry" reinforcement learning? Stability in continuous state-action spacescited by 3
  34. 2024Realizable Continuous-Space Shields for Safe Reinforcement Learningcited by 3
  35. 2021Deep Learning for Embodied Vision Navigation: A Surveycited by 2
  36. 2021Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Modelcited by 2
  37. 2021ROS-X-Habitat: Bridging the ROS Ecosystem with Embodied AIcited by 2
  38. 2022Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluationcited by 2

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