Collective motion in swarm robotics is often designed from homogeneous, physics-inspired rules, yet real swarms must operate in confined arenas where boundaries reshape local interactions and where robots exhibit heterogeneous sensor and motor biases. We introduce an online, fully decentralized self-calibration framework that tunes microscopic control parameters so that the swarm reaches a desired macroscopic motility phase. Building on social learning, we add a fast-transmission mechanism that rapidly disseminates clearly better controllers through local interactions, complementing slower mutation–selection updates. Using a motility model combining alignment, density-dependent crowding, and collective U-turn propagation, we show in simulation that swarms self-calibrate from random initial behavior to reliably achieve several target phases (flocking, disordered gas, clustering, and gas–solid coexistence) in a disk arena. Fast transmission substantially accelerates convergence and improves robustness to heterogeneity, enabling rapid phase control without any central coordination.