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Extraterrestrial autonomous lander missions increasingly demand adaptivecapabilities to handle the unpredictable and diverse nature of the terrain.This paper discusses the deployment of a Deep Meta-Learning with ControlledDeployment Gaps (CoDeGa) trained model for terrain scooping tasks in OceanWorlds Lander Autonomy Testbed (OWLAT) at NASA Jet Propulsion Laboratory. TheCoDeGa-powered scooping strategy is designed to adapt to novel terrains,selecting scooping actions based on the available RGB-D image data and limitedexperience. The paper presents our experiences with transferring the scoopingframework with CoDeGa-trained model from a low-fidelity testbed to thehigh-fidelity OWLAT testbed. Additionally, it validates the method'sperformance in novel, realistic environments, and shares the lessons learnedfrom deploying learning-based autonomy algorithms for space exploration.Experimental results from OWLAT substantiate the efficacy of CoDeGa in rapidlyadapting to unfamiliar terrains and effectively making autonomous decisionsunder considerable domain shifts, thereby endorsing its potential utility infuture extraterrestrial missions.

Learning and Autonomy for Extraterrestrial Terrain Sampling: An Experience Report from OWLAT Deployment