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

arXiv: https://arxiv.org/abs/2402.05066 · HTML: https://mlsanity.com/paper/2402.05066/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [XTENTH-CAR: A Proportionally Scaled Experimental Vehicle Platform for Connected Autonomy and All-Terrain Research](https://mlsanity.com/paper/2212.01691/) (2022)
- [AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning](https://mlsanity.com/paper/2304.11496/) (2023)
- [Deep Reinforcement Learning for Autonomous Ground Vehicle Exploration Without A-Priori Maps](https://mlsanity.com/paper/2301.04036/) (2023)
- [Competitive Driving of Autonomous Vehicles](https://mlsanity.com/paper/2109.05455/) (2021)

## 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.