NavTuner: Learning a Scene-Sensitive Family of Navigation Policies

TL;DR

This paper introduces NavTuner, a method that uses reinforcement learning to adaptively tune navigation policies based on scene structure, improving robustness and performance in cluttered environments.

Contribution

It proposes learning adaptive parameters for navigation modules via reinforcement learning, enabling dynamic reconfiguration based on local scene features.

Findings

RL significantly improves navigation success rate in cluttered environments

Adaptive parameter tuning reduces sensitivity to environmental nuisance factors

NavTuner enhances robustness of existing navigation algorithms

Abstract

The advent of deep learning has inspired research into end-to-end learning for a variety of problem domains in robotics. For navigation, the resulting methods may not have the generalization properties desired let alone match the performance of traditional methods. Instead of learning a navigation policy, we explore learning an adaptive policy in the parameter space of an existing navigation module. Having adaptive parameters provides the navigation module with a family of policies that can be dynamically reconfigured based on the local scene structure, and addresses the common assertion in machine learning that engineered solutions are inflexible. Of the methods tested, reinforcement learning (RL) is shown to provide a significant performance boost to a modern navigation method through reduced sensitivity of its success rate to environmental clutter. The outcomes indicate that RL as a meta-policy learner, or dynamic parameter tuner, effectively robustifies algorithms sensitive to external, measurable nuisance factors.