GAUGE: プランナ条件付き能動校正によるブラックボックス四足歩行速度インタフェースの適応
GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces
四足ロボットの速度指令と実際の運動のずれを、ナビゲーションプランナが使う指令分布に基づくベイズ能動学習で少ない試行で校正するフレームワークを提案。
著者: Tianhao Zang, Zihan Liu, Shanze Wang, Liyou Luo, Xingjian Xie, Chengtai Li, Wei Zhang
分類: cs.RO
原文アブストラクト
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.