The ability to autonomously assemble structures is crucial for the
development of future space infrastructure. However, the unpredictable
conditions of space pose significant challenges for robotic systems,
necessitating the development of advanced learning techniques to enable
autonomous assembly. In this study, we present a novel approach for learning
autonomous peg-in-hole assembly in the context of space robotics. Our focus is
on enhancing the generalization and adaptability of autonomous systems through
deep reinforcement learning. By integrating procedural generation and domain
randomization, we train agents in a highly parallelized simulation environment
across a spectrum of diverse scenarios with the aim of acquiring a robust
policy. The proposed approach is evaluated using three distinct reinforcement
learning algorithms to investigate the trade-offs among various paradigms. We
demonstrate the adaptability of our agents to novel scenarios and assembly
sequences while emphasizing the potential of leveraging advanced simulation
techniques for robot learning in space. Our findings set the stage for future
advancements in intelligent robotic systems capable of supporting ambitious
space missions and infrastructure development beyond Earth.

通过深度强化学习，在高度并行化的仿真环境中，利用程序生成和域随机化的方法，提出了一种新颖的学习自主针孔装配的方法，以增强自主系统的泛化性和适应性，评估了三种不同的强化学习算法，并展示了智能机器人系统在太空中学习中的潜力。