多自由度災害対応ロボットの環境適応性強化に関する研究

～タスク判別と学習済み制御則の再利用に基づく動作学習の効率化～

抄録

The multi-degree-of-freedom disaster response robot ‘OCTOPUS’ requires an ‘environment-adaptive motion generation function’ to fully utilize its advanced ‘body.’ The disaster site is unknown and complex, so there is a limit to the amount of control rules that can be described in advance by humans. To address this issue, we have developed a basic framework for an adaptive reinforcement system that learns behaviors in a virtual space generated based on environmental information acquired in a real space. In this study, we develop a motion learning system based on task classification and reuse of learned control rules. As a result of experiments on rough terrain, we confirmed that the proposed system could accomplish a moving task on any unknown terrain in a shorter time than the conventional system by the task classification and reusing control laws.