行動クローニングと強化学習によるブラシ操作のsim-to-real転移
Sim-to-Real Brush Manipulation using Behavior Cloning and Reinforcement Learning
行動クローニングと強化学習を組み合わせてブラシ操作エージェントを訓練し、シミュレーションから実機のロボットアームへ滑らかに転移させる手法を提案した。
著者: Biao Jia, Dinesh Manocha
分類: cs.RO
原文アブストラクト
Developing proficient brush manipulation capabilities in real-world scenarios is a complex and challenging endeavor, with wide-ranging applications in fields such as art, robotics, and digital design. In this study, we introduce an approach designed to bridge the gap between simulated environments and real-world brush manipulation. Our framework leverages behavior cloning and reinforcement learning to train a painting agent, seamlessly integrating it into both virtual and real-world environments. Additionally, we employ a real painting environment featuring a robotic arm and brush, mirroring the MyPaint virtual environment. Our results underscore the agent's effectiveness in acquiring policies for high-dimensional continuous action spaces, facilitating the smooth transfer of brush manipulation techniques from simulation to practical, real-world applications.
