Robotics: Science and Systems XIX
Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift
Yifan Zhu, Pranay Thangeda, Melkior Ornik, Kris HauserAbstract:
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.
Bibtex:
@INPROCEEDINGS{Zhu-RSS-23, AUTHOR = {Yifan Zhu AND Pranay Thangeda AND Melkior Ornik AND Kris Hauser}, TITLE = {{Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2023}, ADDRESS = {Daegu, Republic of Korea}, MONTH = {July}, DOI = {10.15607/RSS.2023.XIX.048} }