Deep reinforcement learning has recently been applied to a variety of robotics applications, but learning locomotion for robots with unconventional configurations is still limited. Prior work has shown that, despite the simple modeling of articulated swimmer robots, such systems struggle to find effective gaits using reinforcement learning due to the heterogeneity of the search space. In this work, we leverage insight from geometric models of these robots in order to focus on promising regions of the space and guide the learning process. We demonstrate that our augmented learning technique is able to produce gaits for different learning goals for swimmer robots in both low and high Reynolds number fluids.

翻译：深度强化学习最近已被应用于多种机器人任务中，但在非常规构型机器人的运动学习方面仍存在局限性。先前研究表明，尽管关节式游泳机器人建模简单，但由于搜索空间异质性强，这类系统难以通过强化学习找到有效步态。在本工作中，我们利用这些机器人几何模型的洞察，聚焦于空间中的有前途区域并引导学习过程。我们证明，改进后的学习技术能够为低雷诺数和高雷诺数流体中的游泳机器人，针对不同学习目标生成步态。