Abstract:We present Sym2Real, a fully data-driven framework for highly data-efficient adaptation of low-level controllers. Although symbolic regression is data-efficient, its role in real-world control has been limited due to its sensitivity to measurement noise, which corrupts the equations and leads to model degradation when fitted directly on real-world data. Sym2Real addresses this limitation by 1) learning first from low-fidelity simulation, where noise-free trajectories allow symbolic regression to identify the underlying dynamics, and 2) using a small amount of real-world data for targeted residual adaptation to bridge the sim-to-real gap. Using only about 10 trajectories, we achieve robust control of both a quadrotor and a racecar in the real world, without expert knowledge or simulation tuning. Through experimental validation on both platforms, we demonstrate consistent data-efficient adaptation across 6 out-of-distribution sim2sim scenarios and successful sim2real transfer across 5 real-world conditions. More information can be found at this http URL
From: Easop Lee [view email]
[v1]
Thu, 18 Sep 2025 20:39:56 UTC (19,717 KB)
[v2]
Fri, 17 Jul 2026 05:46:04 UTC (19,022 KB)
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