  Extraterrestrial autonomous lander missions increasingly demand adaptive
capabilities to handle the unpredictable and diverse nature of the terrain.
This paper discusses the deployment of a Deep Meta-Learning with Controlled
Deployment Gaps (CoDeGa) trained model for terrain scooping tasks in Ocean
Worlds Lander Autonomy Testbed (OWLAT) at NASA Jet Propulsion Laboratory. The
CoDeGa-powered scooping strategy is designed to adapt to novel terrains,
selecting scooping actions based on the available RGB-D image data and limited
experience. The paper presents our experiences with transferring the scooping
framework with CoDeGa-trained model from a low-fidelity testbed to the
high-fidelity OWLAT testbed. Additionally, it validates the method's
performance in novel, realistic environments, and shares the lessons learned
from deploying learning-based autonomy algorithms for space exploration.
Experimental results from OWLAT substantiate the efficacy of CoDeGa in rapidly
adapting to unfamiliar terrains and effectively making autonomous decisions
under considerable domain shifts, thereby endorsing its potential utility in
future extraterrestrial missions.
