Abstract
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.
Abstract (translated)
自主组装结构的能力对于未来空间基础设施的发展至关重要。然而，空间的不可预测条件对机器人系统提出了重大挑战，需要开发高级学习技术来实现自主组装。在这项研究中，我们提出了一个在空间机器人领域学习自主穿孔桩组装的新方法。我们的重点是通过深度强化学习增强自主系统的泛化能力和适应性。通过将程序生成和领域随机化集成起来，我们在一系列多样场景中训练具有高度并行化的仿真环境中的智能体，旨在获得稳健的策略。所提出的方法通过评估三种不同的强化学习算法，研究了各种范式之间的权衡。我们展示了我们的智能体在应对新颖场景和组装序列时具有的可扩展性，同时强调利用先进的仿真技术进行机器人学习的潜力，为支持太空探索和基础设施发展做好准备。我们的研究为未来智能机器人系统在支持太空探索和基础设施发展方面取得进一步进展奠定了基础。
URL
https://arxiv.org/abs/2405.01134
