GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces

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 this https URL.

Robotics (cs.RO)

arXiv:2609.32154 [cs.RO]

(or arXiv:2609.32154v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.32154 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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