# Dimension-variable Mapless Navigation with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.06320 · HTML: https://mlsanity.com/paper/2002.06320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation](https://mlsanity.com/paper/2610.17927/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained agents are limited to the specific robot dimensions used during training, hindering their applicability when the robot's dimension changes for task-specific requirements. To overcome this limitation, we propose a dimension-variable robot navigation method based on DRL. Our approach involves training a meta agent in simulation and subsequently transferring the meta skill to a dimension-varied robot using a technique called dimension-variable skill transfer (DVST). During the training phase, the meta agent for the meta robot learns self-navigation skills with DRL. In the skill-transfer phase, observations from the dimension-varied robot are scaled and transferred to the meta agent, and the resulting control policy is scaled back to the dimension-varied robot. Through extensive simulated and real-world experiments, we demonstrated that the dimension-varied robots could successfully navigate in unknown and dynamic environments without any retraining. The results show that our work substantially expands the applicability of DRL-based navigation methods, enabling them to be used on robots with different dimensions without the limitation of a fixed dimension. The video of our experiments can be found in the supplementary file.