Vol. 1, No. 3 (September 2026) — Article KAGG:2609.00003

Closed-Loop Field Calibration of Quadruped Velocity Interfaces

Authors: Wei Xu, Arjun Mehta, Ethan Cole

Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA

[Received 2026-08-22; accepted 2026-09-02; published online 2026-09-05]

Abstract:

Quadruped navigation stacks usually command a locomotion controller through a velocity interface whose command-to-motion response must be calibrated before the planner's predictions can be trusted. Open-loop active calibration, such as planner-conditioned probing with GAUGE, yields an accurate model at calibration time, but the response drifts as payload, battery state, temperature, and wear change during deployment. This paper closes the calibration loop. The calibrated interface model is embedded in the local planner, and the prediction errors it incurs during normal operation are treated as passive observations that update a Gaussian posterior over the interface parameters. Active probes are issued only when the planner-relevant predictive uncertainty crosses a threshold, and a residual-decomposition damping rule separates genuine parameter change from structural model mismatch so that the estimator does not chatter when the operating point leaves the model class. The analysis gives conditions under which the posterior covariance and the parameter error remain bounded under bounded mismatch. The estimator was evaluated on an ANYmal B during a three-day field campaign with 18 h of operation, payloads of 0, 6, and 12 kg, battery levels from 100 to 30 percent, and 2.1 M observations logged at 50 Hz. At the same probe frequency of 0.4 probes per hour, navigation success was 90.1 percent, compared with 84.1 percent for recalibration every 4 h and 80.5 percent for a static day-0 model, while the improvement in interface drift was small (2.9 versus 3.0 and 3.4 percent per day).


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Cite this article Wei Xu, Arjun Mehta, Ethan Cole, "Closed-Loop Field Calibration of Quadruped Velocity Interfaces," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 3, KAGG:2609.00003. DOI: 10.5803/kagg.2609.00003.