外观
论文arXiv 2609.055931 个月前
用于自主机器人导航失败预测的自适应代价敏感机器学习：并非所有错误都相同
原文链接：Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal（arXiv 2609.05593 · 发布日期：2026-09-05）
Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal
将导航失败预测重构为后果敏感预测，结合严重度与状态风险加权，并在仿真及UCI SCITOS G5上验证。
核心要点
- 导航失败的后果受速度、障碍距离和感知不确定性影响，错误代价并不相同。
- 方法先用类别严重度调制训练权重，再加入状态相关后果函数和风险敏感项。
- 在2,000个仿真差速驱动episode中，自适应方法提高高严重度召回并降低漏报代价。
- 匹配误报条件下区分优势有限，主要收益来自更保守的运行点；碰撞扩展误报率较高。
验证范围与局限：主要在2,000个仿真差速驱动episode上评估，并用UCI SCITOS G5外部验证；碰撞结果来自受控扩展。
英文摘要 (Abstract)
Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low speed under reliable sensing is not equivalent to the same event during rapid motion, close obstacle approach or degraded perception. This paper reframes navigation failure prediction as consequence-sensitive forecasting. We first establish a fixed baseline in which training weights are modulated by categorical severity, then introduce an adaptive extension defining a state-dependent consequence function combining severity with normalised velocity, obstacle proximity and sensing uncertainty, together with a risk-sensitivity term that rises as conditions deteriorate. We evaluate on 2,000 simulated differential-drive episodes (~1,000,000 timesteps) using episodelevel GroupKFold, with external validation on the UCI SCITOS G5 dataset. Fixed weighting raises Logistic Regression high-severity recall from 0.851 to 0.985 and reduces missed consequence cost from 1,940 to 313; the adaptive extension reaches 0.998 and 82. Under matched false-positive conditions, however, the discriminative advantage is modest (0.986 versus 0.984), so most of the gain reflects a more conservative operating point rather than better ranking. The effect is consistent across all five folds and stable across a threefold span of context coefficients. Because the primary simulation produced no collisions, we add a controlled extension in which 108 of 600 episodes terminate in contact: collision recall rises from 0.850 to 0.966 (fixed) and 0.984 (adaptive), with missed collision cost falling from 1,000 to 105, at false-positive rates of 0.413 and 0.799, respectively. Context-dependent consequence modelling thus provides a principled mechanism for allocating conservatism by physical risk.
作者：Rifa Ferzana
来源
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