# IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning

arXiv: https://arxiv.org/abs/2103.11686 · HTML: https://mlsanity.com/paper/2103.11686/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations](https://mlsanity.com/paper/1805.07095/) (2018)
- [A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments](https://mlsanity.com/paper/1709.08528/) (2017)
- [Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.05027/) (2018)
- [Regularization Matters in Policy Optimization](https://mlsanity.com/paper/1910.09191/) (2019)
- [Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments](https://mlsanity.com/paper/1909.10972/) (2019)
- [Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach](https://mlsanity.com/paper/2010.10209/) (2020)

## Built on by (most cited first, 10 of 10)

- [Advances in Embodied Navigation Using Large Language Models: A Survey](https://mlsanity.com/paper/2311.00530/) (2023)
- [Spatial-VLN: Zero-Shot Vision-and-Language Navigation With Explicit Spatial Perception and Exploration](https://mlsanity.com/paper/2601.12766/) (2026)
- [Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning](https://mlsanity.com/paper/2405.09760/) (2024)
- [Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.15837/) (2024)
- [Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation](https://mlsanity.com/paper/2502.05069/) (2025)
- [Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation](https://mlsanity.com/paper/2503.01146/) (2025)
- [Learning from Demonstration with Failure Awareness for Safe Robot Navigation](https://mlsanity.com/paper/2604.23360/) (2026)
- [Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles](https://mlsanity.com/paper/2503.01127/) (2025)
- [MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation](https://mlsanity.com/paper/2503.23908/) (2025)
- [FlashNav: Training Deployable Robot Navigation Policies in Seconds](https://mlsanity.com/paper/2606.15846/) (2026)

## Abstract (arXiv)

This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL). Although DRL exhibits huge potential in robot mapless navigation, DRL agents performing well in training scenarios are often found to perform poorly in unfamiliar scenarios. In this work, we propose that the representation of LiDAR readings is a key factor behind the degradation of agents' performance and present a powerful input pre-processing (IP) approach to address this issue. As this approach uses adaptively parametric reciprocal functions to pre-process LiDAR readings, we refer to this approach as IPAPRec and its normalized version as IPAPRecN. IPAPRec/IPAPRecN can highlight important short-distance values and compress the range of less-important long-distance values in laser scans, which well address the issues induced by conventional representations of laser scans. Their high performance was validated by extensive simulation and real-world experiments. The results show that our methods can substantially improve navigation agents' generalization performance and greatly reduce the training time compared to conventional methods.