A decentralized tracking controller combines geometric modelling of three-dimensional rigid-body motion with Gaussian-process learning. The learned model estimates uncertain terms in each agent’s dynamics and compensates for them while supporting collision avoidance. The analysis provides a high-probability bound on tracking error, and simulations illustrate how the controller operates. First published online on 31 December 2025; included in the June 2026 issue.