Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space

arXiv:2405.01134v1 Announce Type: cross
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 …

abstract advanced arxiv assembly autonomous challenges cs.ai cs.lg cs.ro development future however infrastructure novel robotic space study systems type