  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.
