Latent Space Curriculum Reinforcement Learning in High-Dimensional Contextual Spaces and Its Application to Robotic Piano Playing

Curriculum reinforcement learning (CRL) enables learning optimal policies in complex tasks such as robotic hand manipulation. However, in tasks with high-dimensional contexts with temporally continuous goals, previous research has encountered issues such as increased computational costs and the inability to create appropriate curricula to facilitate learning. Therefore, this study proposes a novel CRL method that appropriately segments high-dimensional contexts and learns them using a generative model. Additionally, we propose a method to further enhance learning by incorporating difficulty information into the generative model. Finally, we experimentally confirm that our proposed method significantly accelerates learning in complex tasks such as dual-arm dexterous hand tasks, specifically, RoboPianist.