Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift

Yifan Zhu
University of Illinois at Urbana-Champaign
Pranay Thangeda
University of Illinois at Urbana-Champaign
Melkior Ornik
University of Illinois at Urbana-Champaign
Kris Hauser
University of Illinois
Paper Website

Paper ID 48

Session 6. Grasping and Manipulation

Poster Session Wednesday, July 12

Poster 16

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.