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:为了支持日常生活活动,采用软性手外骨骼,需要精确控制外骨骼的手指关节。控制关节遵循给定轨迹的问题称为跟踪控制问题。在本研究中,我们关注人手上带有薄麦克宾肌肉的跟踪控制问题。为了用薄麦克宾肌肉实现精细控制,存在两个问题:其一是肌肉的复杂特性,例如非线性、滞后、现实世界中的不确定性,其二是难以访问准确的肌肉和人手模型。为了解决这些问题,我们采用了Dreamer-V2,这是一种基于模型的强化学习方法,但学习的模型无法生成目标轨迹。因此,我们提出了Tracker,这是Dreamer-V2的扩展,用于跟踪控制问题。在实验中,我们展示了Tracker可以实现与PID相比约81%的更小误差,用于控制模拟人类食指从掌骨到近端骨的部分的二段式机械臂。Tracker通过训练约60分钟实现了对人类食指第三关节的控制,误差很小。此外,需要在将薄麦克宾肌肉拆卸并重新连接后,通过对用户的手指进行预训练的策略进行微调,只需要大约15分钟就可以实现与拆下前的准确性相当。