发表机构
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出自适应代价敏感方法，通过状态相关后果函数预测机器人导航失败，在模拟和真实数据上显著提升高严重性及碰撞召回率，降低遗漏成本。
AI 中文摘要
自主机器人导航失败不仅在类别严重性上有所不同，而且在发生的物理环境上也有所不同。在可靠感知下低速行驶时的险情，与在快速运动、接近障碍物或感知退化时发生的相同事件并不等同。本文将导航失败预测重新定义为后果敏感型预测。我们首先建立一个固定基线，其中训练权重由类别严重性调制，然后引入一个自适应扩展，定义一个状态相关的后果函数，该函数结合了严重性与归一化速度、障碍物接近度和感知不确定性，并包含一个随着条件恶化而上升的风险敏感项。我们在2,000个模拟差速驱动情节（约1,000,000个时间步）上使用情节级GroupKFold进行评估，并在UCI SCITOS G5数据集上进行外部验证。固定加权将逻辑回归的高严重性召回率从0.851提高到0.985，并将遗漏后果成本从1,940降至313；自适应扩展达到0.998和82。然而，在匹配的假阳性条件下，判别优势有限（0.986对0.984），因此大部分收益反映了一个更保守的操作点，而非更好的排序。该效果在所有五个折中一致，并在三倍范围的上下文系数下保持稳定。由于主要模拟未产生碰撞，我们添加了一个受控扩展，其中600个情节中的108个以接触终止：碰撞召回率从0.850提高到0.966（固定）和0.984（自适应），遗漏碰撞成本从1,000降至105，假阳性率分别为0.413和0.799。因此，上下文相关的后果建模为按物理风险分配保守性提供了一种原则性机制。
英文摘要
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 episode-level 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.
Comments14 pages, 9 tables
