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：外星天体上的自主着陆器需要在应对域偏移的同时采集颗粒状物质，无论在地球上如何精心调整采样策略，这一挑战始终存在。本文提出了一种自适应铲取策略，利用基于元学习训练的深度高斯过程方法，在目标地形上从极有限的在线经验中学习。该方法引入了一种新的元训练方法——带控制部署间隙的深度元学习（CoDeGa），明确训练深度核函数，使其能够在较大域偏移下鲁棒地预测铲取体积。结合贝叶斯优化的序贯决策框架，该方法使机器人能够仅凭视觉和极少的在线经验，在分布外地形上实现高质量的铲取动作，显著优于挖掘文献中提出的非自适应方法以及其他最先进的元学习方法。此外，本文公开了一个包含6,700次执行铲取的数据集，这些数据涵盖多种材料、地形地貌和成分，为未来颗粒状物质操纵与元学习研究提供了基础。