Fleet-To-Lab: モデル融合による月面ローバのスリップ推定のための転移学習フレームワーク
Fleet-To-Lab: A Transfer Learning Framework For Lunar Rover Slippage Estimation Via Model Fusion
過去の月面ローバの固有感覚データを活用し、地球と月のドメインギャップを埋める転移学習フレームワークを提案。複数の異種専門モデルを群知能アルゴリズムAcoMergeで融合し、スリップ推定精度を向上させた。
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著者: Riccardo Viviano, Saki Omi, Andrej Orsula, Miguel Olivares-Mendez
分類: cs.RO
原文アブストラクト
Accurate wheel slip estimation is essential for autonomous lunar rover mobility and navigation. Machine Learning models trained on terrestrial data generalize poorly to lunar terrain, and real lunar datasets are scarce due to the limited number of missions and costly data acquisition. We present Fleet-to-Lab, a transfer learning framework that leverages proprioceptive data collected by previously deployed heterogeneous lunar rovers to mitigate the Earth-Moon domain gap in slip estimation for a future deployable unit. We fuse several heterogeneous expert models into a single architecture, using a modest dataset collected after the rover deployment. We propose AcoMerge, a new hybrid swarm-intelligence algorithm that performs model fusion by searching for an optimal combi- nation of expert parameters. Experiments conducted in a high- fidelity physics simulation show balanced accuracy and macro- F1 improvements compared to deep model fusion baselines. AcoMerge exhibits competitive performance with joint training on deep architectures, while achieving higher macro-F1 and balanced accuracy on a smaller model. Overall, our framework shows model fusion as a possible transfer learning alternative for slippage estimation in space robotic missions with limited data.
