EffiTune: Diagnosing and Mitigating Training Inefficiency for Parameter Tuner in Robot Navigation System

TL;DR

EffiTune tackles training inefficiency in parameter tuners for robot navigation by diagnosing bottlenecks through robot-behavior analysis and enriching training data with targeted up-sampling of high-resistance regions. The framework identifies critical bottlenecks using high-resistance area detection and mitigates them via iterative, threshold-guided sampling, leading to improved robustness and faster tuner convergence. Empirical results show a $>13.5\%$ improvement in navigation performance, better out-of-distribution robustness, and a $\times 4$ increase in training efficiency within the same budget. This approach promises practical benefits for deploying adaptive, data-driven tuners in real-world navigation tasks by reducing costly exploration while preserving safety and efficiency.

Abstract

Robot navigation systems are critical for various real-world applications such as delivery services, hospital logistics, and warehouse management. Although classical navigation methods provide interpretability, they rely heavily on expert manual tuning, limiting their adaptability. Conversely, purely learning-based methods offer adaptability but often lead to instability and erratic robot behaviors. Recently introduced parameter tuners aim to balance these approaches by integrating data-driven adaptability into classical navigation frameworks. However, the parameter tuning process currently suffers from training inefficiencies and redundant sampling, with critical regions in environment often underrepresented in training data. In this paper, we propose EffiTune, a novel framework designed to diagnose and mitigate training inefficiency for parameter tuners in robot navigation systems. EffiTune first performs robot-behavior-guided diagnostics to pinpoint critical bottlenecks and underrepresented regions. It then employs a targeted up-sampling strategy to enrich the training dataset with critical samples, significantly reducing redundancy and enhancing training efficiency. Our comprehensive evaluation demonstrates that EffiTune achieves more than a 13.5% improvement in navigation performance, enhanced robustness in out-of-distribution scenarios, and a 4x improvement in training efficiency within the same computational budget.

Figures (12)

• Figure 1: Different Types of Robot Navigation Systems. Parameter tuners are typically data-driven and requires training.
• Figure 2: Maps of various difficulty levels. We use the difficulty metric of "normalized traversal time" (traversal time averaged over the ten trials and normalized by path length) from perille2020benchmarking to partition map datasets to 3 levels.
• Figure 3: Training inefficiency in existing parameter tuner. For convenient visualization, we show 2D projections of the difficult map from Fig. \ref{['fig:gazebo_3d']} and omit the robot body. The yellow arrow denotes the start position and the yellow circle denotes the goal. Red cross markers are the robot trajectories at each timestamp (each subfigure contains 3 trajectories to show the distribution). On top and right of the maps are the frequency distribution of robot positions $x$ and $y$.
• Figure 4: Overview of EffiTune Framework
• Figure 5: Comparison of RandomSampling and EffiTune. Parameter tuners are tested on all 3 levels.