空间机器人操作：一种多任务学习视角

利用空间机器人辅助、代替航天员完成在轨服务操作是近年的技术发展趋势。基于学习的空间机器人操作以深度神经网络为控制器载体，对非结构化太空环境适应能力强，在高轨、地外、深空等场景具有良好应用前景。目前，无论是空间机器人操作，还是地面机器人操作，多数研究只关注单一任务学习问题。立足一种多任务学习新视角，针对空间机器人操作面临的多任务适应性要求高、精细化要求高、不确定性强问题，首先分析了在轨服务的多样化任务需求。其次，全面综述了机器人操作多任务学习算法与应用相关工作，分析了开展空间机器人操作多任务学习的难点挑战，给出了关键技术发展建议。相关关键技术的突破将有助于提升空间机器人系统的自主性、鲁棒性，进而助力中国在轨服务技术向无人全自主方向推进。

Space robotic manipulation: a multi-task learning perspective

It is a technological development trend in recent years to apply space robot in place of spaceman to perform on-orbit service tasks. Using deep neural network controller, the learning-based space robotic manipulation has shown good potential in adaptability to the unstructured space environments and applicability in fileds such as high earth orbit, extraterrestrial planet exploration, etc. At present, a large number of studies focus on single task robotic manipulation learning problems, for either on ground robots or in-space robots. From a new perspective of multi-task learning, a thorough literature review on multi-task robot learning was made, including algorithms and robotic applications therein. To further apply the state-of-the-art multi-task robot learning algorthms, main technical challenges were analyzed and suggestions on key technology development were given. The breakthrough of the above challenges will increase the overall autonomy and robustness level of the space robot system, which is expected to further facilitate the development of China′s on-orbit service towards completely unmanned autonomy.