边际校准不可组合：模块化机器人导航中的隐藏依赖性
Marginal Calibration Does Not Compose: Hidden Dependence in Modular Robot Navigation
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中文总结 AI 辅助
本研究揭示模块化机器人导航中，各模块独立校准良好但组合后系统级校准失效的问题，提出建模联合协方差以恢复校准并提升性能，强调接口需传递依赖信息或支持系统级校准。
中文摘要 AI 辅助
机器人系统通常由多个独立开发的模块组成，这些模块协同工作以感知、预测并在环境中行动。尽管每个模块在单独运行时可能表现可靠，但组合它们并不一定能保持系统层面的不确定性校准。在本工作中，我们表明，经过良好校准的组件接口在组合后并不一定能产生校准良好的下游行为。通过一个移动障碍物预测流水线，我们证明位置和速度估计器各自可能看起来单独校准良好，然而它们的误差相关方式的差异会导致对未来状态不确定性的估计产生显著不同。因此，假设独立性可能使系统要么过度自信，要么不必要地保守，直接影响下游规划决策和安全性。通过模拟，我们表明建模联合协方差可以恢复下游校准并提高系统性能，而依赖鲁棒的不确定性边界在增加保守性的代价下增强安全性。我们的发现揭示了独立验证机器人模块的根本局限性，并强调了需要能够传递依赖性信息或支持直接系统级校准的接口。
英文摘要
Robotic systems are typically composed of multiple independently developed modules that work together to perceive, predict, and act in the environment. Although each module may perform reliably in isolation, composing them does not necessarily preserve uncertainty calibration at the system level. In this work, we show that well-calibrated component interfaces do not necessarily produce calibrated downstream behavior after composition. Using a moving-obstacle prediction pipeline, we demonstrate that position and velocity estimators can each appear well calibrated individually, yet differences in how their error are correlated lead to substantially different estimates of future-state uncertainty. Consequently, assuming independence can make the system either overly confident or unnecessarily conservative, directly influencing downstream planning decisions and safety. Through simulations, we show that modeling the joint covariance restores downstream calibration and improves system performance, whereas dependence-robust uncertainty bounds enhance safety at the cost of increased conservatism. Our findings reveal a fundamental limitation of independently validating robotic modules and highlight the need for interfaces that communicate dependence information or support direct system-level calibration.
发表机构
- University of Delaware（特拉华大学）
机构由 AI 辅助整理，请以论文原文为准。
