  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.
