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Updated: Jan 14, 2026
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
Published on: August 26, 2018
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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.
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.
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