AI 中文总结
本文提出基于Kolmogorov-Arnold网络的TravKAN框架，结合LiDAR反射率手工特征，在公开数据集上性能优于传统深度模型，兼具可解释性与高效性，适用于安全关键的机器人可通行性分析。
AI 中文摘要
可通行性分析是自主移动机器人在非结构化环境中作业的基础能力。尽管深度神经网络、梯度提升树等现代机器学习方法具备出色的预测性能，但它们缺乏可解释性，难以深入理解地形-机器人交互的底层动态。本文提出TravKAN，一种基于Kolmogorov-Arnold网络的框架，用于快速、可扩展且可解释的可通行性估计。TravKAN通过可学习单变量函数的组合表示多变量决策函数，实现紧凑的架构，并可在训练后提取解析表达式。此外，本文引入一组新的手工特征，这些特征源自LiDAR传感器的反射率通道。据所知，尽管反射率在捕捉与几何线索互补的材料和表面属性方面具有潜力，但尚未被系统地用于手工可通行性描述符。本文在公开的真实城市和越野数据集上对TravKAN进行评估，并与强基线方法对比。TravKAN在所有指标上均表现出色，优于传统深度模型，且性能接近XGBoost。TravKAN-Lite（即TravKAN的符号表示）揭示了有意义的非线性特征交互，提供了紧凑、易于部署且快速的解析模型。 ablation研究进一步证明了本文方法对架构变化的鲁棒性，并量化了所提基于反射率特征的贡献。这些特性使TravKAN适用于对安全关键决策要求透明、实时计算效率和可解释性的机器人系统。
英文摘要
Traversability analysis is a fundamental capability for autonomous mobile robots operating in unstructured environments. While modern machine learning approaches such as deep neural networks and gradient-boosted trees achieve strong predictive performance, they lack interpretability and provide limited insight into the underlying terrain-robot interaction dynamics. In this paper, we propose TravKAN, a Kolmogorov-Arnold Network-based framework for fast, scalable, and interpretable traversability estimation. TravKAN represents multivariate decision functions through compositions of learnable univariate functions, enabling compact architectures and symbolic extraction of analytic expressions after training. In addition, we introduce a novel set of handcrafted features derived from the reflectivity channel of LiDAR sensors. To the best of our knowledge, reflectivity has not been systematically exploited for handcrafted traversability descriptors, despite its potential to capture material and surface properties complementary to geometric cues. We evaluate TravKAN on public, real-world urban and off-road datasets and compare it against strong baselines. TravKAN achieves strong performance across all metrics, outperforming conventional deep models and approaching the performance of XGBoost. TravKAN-Lite, i.e., TravKAN's symbolic representation, reveals meaningful nonlinear feature interactions and provides a compact, deployment-friendly, and fast analytic model. Ablation studies further show the robustness of our method to architectural variations and quantify the contribution of the proposed reflectivity-based features. These properties make TravKAN attractive for robotic systems requiring transparency, real-time computational efficiency, and interpretability in safety-critical decision-making.
CommentsThis paper has been accepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
