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自适应能力，以应对地形的不可预测性和多样性。本文探讨了在NASA喷气推进实验室的海洋世界着陆器自主测试平台（OWLAT）上，部署一种基于深度元学习且具有可控部署差距（CoDeGa）的训练模型用于地形铲取任务。CoDeGa驱动的铲取策略旨在适应新地形，基于可用的RGB-D图像数据和有限经验选择铲取动作。本文介绍了将搭载CoDeGa训练模型的铲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，本文验证了该方法在新颖逼真环境中的性能，并分享了在太空探索中部署基于学习的自主算法所获得的教训。OWLAT的实验结果证实了CoDeGa在快速适应陌生地形以及在显著域偏移下有效做出自主决策方面的效力，从而验证了其在未来地外任务中的潜在应用价值。