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

arXiv: https://arxiv.org/abs/1703.00420 · HTML: https://mlsanity.com/paper/1703.00420/

## Key facts

- Cited by 98 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 30 of 98)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Memory-based Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/2102.12344/) (2021)
- [Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion](https://mlsanity.com/paper/2308.12517/) (2023)
- [MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments](https://mlsanity.com/paper/2203.07060/) (2022)
- [Verifying Learning-Based Robotic Navigation Systems](https://mlsanity.com/paper/2205.13536/) (2022)
- [DiWA: Diffusion Policy Adaptation with World Models](https://mlsanity.com/paper/2508.03645/) (2025)
- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2102.03483/) (2021)
- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [Safe Reinforcement Learning Using Black-Box Reachability Analysis](https://mlsanity.com/paper/2204.07417/) (2022)
- [Formally Explaining Neural Networks within Reactive Systems](https://mlsanity.com/paper/2308.00143/) (2023)
- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (2025)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [Causal Policy Gradient for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2305.04866/) (2023)
- [State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2207.04361/) (2022)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [Verification-Guided Shielding for Deep Reinforcement Learning](https://mlsanity.com/paper/2406.06507/) (2024)
- [Curriculum Learning for Safe Mapless Navigation](https://mlsanity.com/paper/2112.12490/) (2021)
- [High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization](https://mlsanity.com/paper/2206.08487/) (2022)
- [Constrained Reinforcement Learning for Robotics via Scenario-Based Programming](https://mlsanity.com/paper/2206.09603/) (2022)
- [Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation](https://mlsanity.com/paper/2103.01391/) (2021)
- [IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning](https://mlsanity.com/paper/2103.11686/) (2021)
- [Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping](https://mlsanity.com/paper/2209.14271/) (2022)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [Training a Resilient Q-Network against Observational Interference](https://mlsanity.com/paper/2102.09677/) (2021)
- [Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces](https://mlsanity.com/paper/2107.01667/) (2021)
- [Safe Deep Reinforcement Learning by Verifying Task-Level Properties](https://mlsanity.com/paper/2302.10030/) (2023)
- [ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics](https://mlsanity.com/paper/2411.18825/) (2024)
- [Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.00331/) (2023)
- [PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/2310.13295/) (2023)

## 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.