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TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 2 (August 2026) > KAGG:2608.00001
Vol. 1, No. 2 (August 2026) — Article KAGG:2608.00001

Information-Directed Active Calibration of Unknown Velocity Dynamics

Authors: Daniel Okafor, Priya Raghunathan, Hana Sato

Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA

[Received 2026-07-19; accepted 2026-07-30; published online 2026-08-02]

Abstract:

Navigation stacks for legged robots usually command a vendor locomotion controller through an opaque velocity interface whose gains, saturation limits, and latency differ from their nominal values and must be calibrated before a planner can trust its motion model. Calibration requires choosing probe commands, and uniform excitation spends much of its motion on directions whose response is already well determined. This paper formulates probe selection as information-directed sampling. A particle posterior is maintained over a six-parameter state-delay model of the interface, comprising three per-axis gains, a planar-speed and a yaw-rate saturation limit, and a transport delay, and each probe is chosen to maximize the expected information gain about these parameters per unit of commanded path length. The information gain is evaluated through the saturated model, and a hierarchical shrinkage prior couples the per-axis gains so that weakly excited directions borrow strength from well-excited ones. The method is compared with uniform sweep excitation, random probing under a motion budget, and the planner-conditioned probes of GAUGE over 200 simulated calibration sessions of a Unitree B2 in Isaac Lab, with validation on five real-robot sessions. Information-directed probing attains a worst-direction calibration error of 8.9 percent against 9.5 to 10.3 percent for the baselines, reaches navigation-grade accuracy in 38 s against 40 to 42 s, and uses 11.4 m of commanded motion against 11.8 to 13.0 m. Ablations indicate that cost weighting, saturation awareness, and shrinkage are each needed to outperform uniform excitation.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 2 (August 2026)
Article ID: KAGG:2608.00001
DOI: 10.5803/kagg.2608.00001
Received: 2026-07-19
Accepted: 2026-07-30
Published online: 2026-08-02
License: CC BY 4.0

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Cite this article Daniel Okafor, Priya Raghunathan, Hana Sato, "Information-Directed Active Calibration of Unknown Velocity Dynamics," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 2, KAGG:2608.00001. DOI: 10.5803/kagg.2608.00001.