宇宙での自律ペグインホール組立に向けた手続き的生成の活用
Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space
手続き的生成とドメインランダム化を組み合わせ、多様なシミュレーション環境で深層強化学習エージェントを訓練し、宇宙での自律的なペグインホール組立の汎化性と適応性を向上させる手法を提案した。
著者: Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez, Carol Martinez
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
