# Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments

arXiv: https://arxiv.org/abs/1909.10972 · HTML: https://mlsanity.com/paper/1909.10972/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Residual Reinforcement Learning from Demonstrations](https://mlsanity.com/paper/2106.08050/) (2021)
- [Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics](https://mlsanity.com/paper/2107.09822/) (2021)
- [IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning](https://mlsanity.com/paper/2103.11686/) (2021)
- [Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty](https://mlsanity.com/paper/2106.04306/) (2021)
- [Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping](https://mlsanity.com/paper/2410.02475/) (2024)
- [Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors](https://mlsanity.com/paper/2406.19768/) (2024)
- [RESPRECT: Speeding-up Multi-fingered Grasping with Residual Reinforcement Learning](https://mlsanity.com/paper/2401.14858/) (2024)
- [Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2406.08238/) (2024)
- [Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements](https://mlsanity.com/paper/2505.13837/) (2025)
- [Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots](https://mlsanity.com/paper/2112.05299/) (2021)
- [Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids](https://mlsanity.com/paper/2506.19353/) (2025)

## Abstract (arXiv)

In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones. We present an extension of the residual reinforcement learning framework from the robotic manipulation literature and adapt it to the vast and unstructured environments that mobile robots can operate in. The concept is based on learning a residual control effect to add to a typical sub-optimal classical controller in order to close the performance gap, whilst guiding the exploration process during training for improved data efficiency. We exploit this tight coupling and propose a novel deployment strategy, switching Residual Reactive Navigation (sRRN), which yields efficient trajectories whilst probabilistically switching to a classical controller in cases of high policy uncertainty. Our approach achieves improved performance over end-to-end alternatives and can be incorporated as part of a complete navigation stack for cluttered indoor navigation tasks in the real world. The code and training environment for this project is made publicly available at https://sites.google.com/view/srrn/home.