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.

翻译：地外自主着陆任务日益需要适应能力以应对地形不可预测且多样化的特性。本文讨论了在NASA喷气推进实验室的海洋世界着陆器自主测试平台（OWLAT）中，部署基于深度元学习与受控部署间隙（CoDeGa）训练的模型用于地形勺取任务。基于CoDeGa的勺取策略旨在适应新型地形，根据可用的RGB-D图像数据和有限经验选择勺取动作。本文介绍了将搭载CoDeGa训练模型的勺取框架从低保真测试平台迁移到高保真OWLAT测试平台的经验。此外，它还验证了该方法在新型逼真环境中的性能，并分享了从部署基于学习的自主算法进行太空探索中汲取的教训。OWLAT的实验结果证实了CoDeGa在快速适应陌生地形并有效应对显著领域偏移做出自主决策方面的有效性，从而证明了其在未来地外任务中的潜在应用价值。