To adopt the soft hand exoskeleton to support activities of daily livings, it is necessary to control finger joints precisely with the exoskeleton. The problem of controlling joints to follow a given trajectory is called the tracking control problem. In this study, we focus on the tracking control problem of a human finger attached with thin McKibben muscles. To achieve precise control with thin McKibben muscles, there are two problems: one is the complex characteristics of the muscles, for example, non-linearity, hysteresis, uncertainties in the real world, and the other is the difficulty in accessing a precise model of the muscles and human fingers. To solve these problems, we adopted DreamerV2, which is a model-based reinforcement learning method, but the target trajectory cannot be generated by the learned model. Therefore, we propose Tracker, which is an extension of DreamerV2 for the tracking control problem. In the experiment, we showed that Tracker can achieve an approximately 81% smaller error than PID for the control of a two-link manipulator that imitates a part of human index finger from the metacarpal bone to the proximal bone. Tracker achieved the control of the third joint of the human index finger with a small error by being trained for approximately 60 minutes. In addition, it took approximately 15 minutes, which is less than the time required for the first training, to achieve almost the same accuracy by fine-tuning the policy pre-trained by the user's finger after taking off and attaching thin McKibben muscles again as the accuracy before taking off.

翻译：为将软体手外骨骼用于日常生活活动支持，需要借助外骨骼精确控制手指关节。控制关节跟踪给定轨迹的问题称为跟踪控制问题。本研究聚焦于附着细McKibben肌肉的人手指的跟踪控制问题。利用细McKibben肌肉实现精确控制面临两大挑战：一是肌肉的复杂特性（如非线性、迟滞性和现实世界中的不确定性），二是难以获取肌肉与人手指的精确模型。为解决这些问题，我们采用了基于模型的强化学习方法DreamerV2，但该学习模型无法生成目标轨迹。为此，我们提出Tracker——针对跟踪控制问题对DreamerV2的扩展。实验表明，在控制模拟人类食指从掌骨到近节指骨的二连杆机械臂时，Tracker的误差比PID控制器低约81%。经过约60分钟训练，Tracker能以较小误差完成人类食指第三关节的控制。此外，当用户摘下并重新附着细McKibben肌肉后，通过对用户手指预训练策略进行微调，仅需约15分钟（少于首次训练时间）即可达到与摘除前几乎相同的精度。