AI 中文总结
GAUGE框架通过贝叶斯建模和不确定性引导采样，对不透明四足机器人速度接口进行规划器条件主动校准，以更少试验实现任务精度与不确定性标准，并满足非劣效性要求。
AI 中文摘要
本文提出了一种目标感知的不确定性引导探索（GAUGE）框架，用于对不透明的四足机器人速度接口进行规划器条件主动校准。商用四足机器人通常暴露平面速度命令，但底层运动控制器不可访问，可能导致命令运动与实际运动之间的系统性偏差。导航规划器通常使用命令包络的结构化子集。GAUGE维护一个贝叶斯命令到运动模型，并根据在规划器诱导的命令分布下后验认知不确定性的预期减少来选择授权试验。得到的后验支持基于验证的停止、有界逆补偿以及检测到接口偏移后的任务相关重新校准。在三个受控响应族中，GAUGE以比被动、D最优和任务无关替代方案更少的试验达到任务面向准确性和不确定性的联合标准。在六个保留的Isaac Sim导航地图中，它满足针对密集校准的声明非劣效性边际。代码可在该https URL获取。
英文摘要
In this paper, we present a Goal-Aware Uncertainty-Guided Exploration (GAUGE) framework for planner-conditioned active calibration of opaque quadruped velocity interfaces. Commercial quadrupeds commonly expose planar-velocity commands, but the underlying locomotion controller remains inaccessible and can produce systematic discrepancies between commanded and realized motion. A navigation planner typically uses a structured subset of the command envelope. GAUGE maintains a Bayesian command-to-motion model and selects authorized trials according to their expected reduction of posterior epistemic uncertainty under the planner-induced command distribution. The resulting posterior supports validation-based stopping, bounded inverse compensation, and task-relevant recalibration after detected interface shifts. In three controlled response families, GAUGE reaches the joint criterion for task-facing accuracy and uncertainty with fewer trials than passive, D-optimal, and task-agnostic alternatives. Across six held-out Isaac Sim navigation maps, it meets the declared noninferiority margins against dense calibration. Code is available at https://github.com/EurekaZang/CalibAgent.
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