外观
论文arXiv 2609.321541 个月前
GAUGE：面向不透明四足机器人速度接口的规划器条件化主动校准
原文链接：GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces（arXiv 2609.32154 · 发布日期：2026-09-26）
GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces
GAUGE针对商业四足速度命令与实际运动偏差，基于贝叶斯模型主动选试验，并在受控响应族和Isaac Sim导航地图验证。
核心要点
- 商业四足常开放平面速度命令，但底层控制器不可访问，导致命令与实际运动存在系统偏差。
- GAUGE维护贝叶斯命令到运动模型，并按规划器诱导的命令分布选择授权试验以降低后验认知不确定性。
- 在三个受控响应族中，GAUGE比被动、D-optimal和任务无关替代方法用更少试验达到联合准则。
- 在六个留出的Isaac Sim导航地图上，GAUGE满足相对密集校准的声明非劣界限；代码已开源。
验证范围与局限：验证范围限于三个受控响应族及六个留出的Isaac Sim导航地图。
英文摘要 (Abstract)
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.
作者：Tianhao Zang, Zihan Liu, Shanze Wang, Liyou Luo, Xingjian Xie, Chengtai Li, Wei Zhang
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