Autonomous lander missions on extraterrestrial bodies will need to sample granular material while coping with domain shift, no matter how well a sampling strategy is tuned on Earth. This paper proposes an adaptive scooping strategy that uses deep Gaussian process method trained with meta-learning to learn on-line from very limited experience on the target terrains. It introduces a novel meta-training approach, Deep Meta-Learning with Controlled Deployment Gaps (CoDeGa), that explicitly trains the deep kernel to predict scooping volume robustly under large domain shifts. Employed in a Bayesian Optimization sequential decision-making framework, the proposed method allows the robot to use vision and very little on-line experience to achieve high-quality scooping actions on out-of-distribution terrains, significantly outperforming non-adaptive methods proposed in the excavation literature as well as other state-of-the-art meta-learning methods. Moreover, a dataset of 6,700 executed scoops collected on a diverse set of materials, terrain topography, and compositions is made available for future research in granular material manipulation and meta-learning.

翻译：外星体自主着陆任务需在应对域偏移的同时完成颗粒物料采样 —— 无论地球上的采样策略调试得多么完善。本文提出一种自适应舀取策略，采用基于元学习训练的深度高斯过程方法，在目标地形上通过极少经验进行在线学习。该研究引入新型元训练方法"具有受控部署间隙的深度元学习"(CoDeGa)，通过显式训练深度核函数，使其能够在较大域偏移下稳健预测舀取体积。该方法部署于贝叶斯优化序贯决策框架中，使机器人能够借助视觉与极少量在线经验，在分布外地形上实现高质量舀取动作，显著优于挖掘文献中提出的非自适应方法及其他先进元学习方法。此外，本研究提供了包含6,700次实际舀取操作的数据集（覆盖多样化物料、地形地貌与组成），为颗粒物料操控与元学习的后续研究提供支撑。