# Learning Resilient Behaviors for Navigation Under Uncertainty

arXiv: https://arxiv.org/abs/1910.09998 · HTML: https://mlsanity.com/paper/1910.09998/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

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

- [Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation](https://mlsanity.com/paper/2104.03111/) (2021)
- [Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning](https://mlsanity.com/paper/2009.14509/) (2020)
- [Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2404.16879/) (2024)
- [Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning](https://mlsanity.com/paper/2203.14749/) (2022)
- [Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.10655/) (2024)
- [Towards Dependable Autonomous Systems Based on Bayesian Deep Learning Components](https://mlsanity.com/paper/2301.05297/) (2023)
- [ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks](https://mlsanity.com/paper/2506.22423/) (2025)
- [Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments](https://mlsanity.com/paper/2409.19459/) (2024)
- [Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners](https://mlsanity.com/paper/2410.12232/) (2024)
- [Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation](https://mlsanity.com/paper/2606.05437/) (2026)
- [ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning](https://mlsanity.com/paper/2609.20982/) (2026)

## Abstract (arXiv)

Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is that the learned policy cannot perform flexible and resilient behaviors as traditional methods to adapt to diverse environments. In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions. We present a novel approach for uncertainty-aware navigation by introducing an uncertainty-aware predictor to model the environmental uncertainty, and we propose a novel uncertainty-aware navigation network to learn resilient behaviors in the prior unknown environments. To train the proposed uncertainty-aware network more stably and efficiently, we present the temperature decay training paradigm, which balances exploration and exploitation during the training process. Our experimental evaluation demonstrates that our approach can learn resilient behaviors in diverse environments and generate adaptive trajectories according to environmental uncertainties.