Robotics: Science and Systems XIX

Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift

Yifan Zhu, Pranay Thangeda, Melkior Ornik, Kris Hauser

Abstract:

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.

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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} 
}