Learning Resilient Behaviors for Navigation Under Uncertainty

2019/10/22 by Tingxiang Fan, Pinxin Long, Fan, Tingxiang +9 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.1910.09998

openalex publication_date 2019/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Adam: A Method for Stochastic Optimization
• End to End Learning for Self-Driving Cars
• Uncertainty-Aware Reinforcement Learning for Collision Avoidance
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
• Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Cited by

• ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks

Related

• Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning
• Uncertainty-Aware Reinforcement Learning for Collision Avoidance
• Safe Reinforcement Learning with Model Uncertainty Estimates
• Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception
• GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments