  Autonomous lander missions on extraterrestrial bodies need to sample granular
materials while coping with domain shifts, even when sampling strategies are
extensively tuned on Earth. To tackle this challenge, this paper studies the
few-shot scooping problem and proposes a vision-based adaptive scooping
strategy that uses the deep kernel Gaussian process method trained with a novel
meta-training strategy to learn online from very limited experience on
out-of-distribution target terrains. Our Deep Kernel Calibration with Maximal
Deployment Gaps (kCMD) strategy explicitly trains a deep kernel model to adapt
to large domain shifts by creating simulated maximal deployment gaps from an
offline training dataset and training models to overcome these deployment gaps
during training. Employed in a Bayesian Optimization sequential decision-making
framework, the proposed method allows the robot to perform high-quality
scooping actions on out-of-distribution terrains after a few attempts,
significantly outperforming non-adaptive methods proposed in the excavation
literature as well as other state-of-the-art meta-learning methods. The
proposed method also demonstrates zero-shot transfer capability, successfully
adapting to the NASA OWLAT platform, which serves as a state-of-the-art
simulator for potential future planetary missions. These results demonstrate
the potential of training deep models with simulated deployment gaps for more
generalizable meta-learning in high-capacity models. Furthermore, they
highlight the promise of our method in autonomous lander sampling missions by
enabling landers to overcome the deployment gap between Earth and
extraterrestrial bodies.
