mlsanityData to 14 Oct 2026

Mobile Robot Exploration Without Maps via Out-of-Distribution Deep Reinforcement Learning

arXiv 2402.05066 ↗First posted 7 Feb 2024cs.ROcited by 0

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

Abstract (arXiv)

Autonomous Mobile Robot (AMR) navigation in dynamic environments that may be GPS denied, without a-priori maps, is an unsolved problem with potential to improve humanity's capabilities. Conventional modular methods are computationally inefficient, and require explicit feature extraction and engineering that inhibit generalization and deployment at scale. We present an Out-of-Distribution (OOD) Deep Reinforcement Learning (DRL) approach that includes functionality in unstructured terrain and dynamic obstacle avoidance capabilities. We leverage accelerated simulation training in a racetrack with a transition probability to parameterize spatial reasoning with intrinsic exploratory behavior, in a compact, computationally efficient Artificial Neural Network (ANN), which we transfer zero-shot with a reward component to mitigate differences between simulation and real world physics. Our approach enables utility without a separate high-level planner or real-time cartography and utilizes a fraction of the computation resources of modular methods, enabling execution in a range of AMRs with different embedded computer payloads.

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 12 of its 24 references that are in the map. First posted on arXiv 7 Feb 2024.

Builds on

12 of 24 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 411
  5. 2023NoMaD: Goal Masked Diffusion Policies for Navigation and Explorationcited by 248
  6. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
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  10. 2023AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learningcited by 3
  11. 2023Deep Reinforcement Learning for Autonomous Ground Vehicle Exploration Without A-Priori Mapscited by 2
  12. 2021Competitive Driving of Autonomous Vehiclescited by 1

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  4. 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
  5. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  6. 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation
  7. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
  8. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation