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.

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