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Related Experiment Video

Updated: Jan 14, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Trustworthy navigation with variational policy in deep reinforcement learning.

Karla Bockrath¹, Liam Ernst¹, Rohaan Nadeem¹

¹Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States.

Frontiers in Robotics and AI

|October 24, 2025

Summary

This study introduces Trust-Nav, a new framework for trustworthy navigation in mobile robots using deep reinforcement learning (DRL). Trust-Nav quantifies uncertainty for safer navigation in unknown and dynamic environments.

Keywords:

deep reinforcement learning moment propagation robot uncertainty trustworthy navigation variational policy

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Developing reliable navigation for mobile robots in dynamic environments is challenging.
Deep Reinforcement Learning (DRL) struggles with uncertainty estimation in real-world applications.
Autonomous navigation requires robust obstacle avoidance and mapping without prior knowledge.

Purpose of the Study:

Introduce a novel trustworthy navigation framework, Trust-Nav.
Quantify uncertainty in robot action, localization, and map representation.
Enhance the safety and reliability of DRL-based navigation systems.

Main Methods:

Utilize variational policy learning with Bayesian variational approximation.
Combine policy-based and value-based learning for action guidance.
Embed uncertainty in the reward function using Optimal Experimental Design principles.

Main Results:

Demonstrate superior performance of Trust-Nav in Gazebo simulations.
Achieve robust autonomous navigation and mapping capabilities.
Outperform deterministic DRL approaches in noisy and adversarial conditions.

Conclusions:

Trust-Nav provides safer and more reliable navigation by integrating uncertainty.
The framework enables mobile robots to recognize and respond to their limitations.
Represents a step towards deployable, self-aware robotic systems.