While articulated robots are very versatile and can perform a variety of tasks, they are often redundant systems when viewed from a task-based perspective. In exchange for versatility, the development of task-specific hardware can be created at low cost and often has a beneficial impact on social implementation. In this study, we focused on a robot for dressing assistance, which is known as a complex task, and performed imitation learning using an articulated robot. We then created a task-specific, low-DoF hardware prototype based on the low-dimensional feature space acquired through imitation learning. The results of the prototype demonstration showed that the system can acquire motions that can be adapted to a round-back posture and a standard posture by changing the motor speed based on the information stretched and compressed in the time axis direction. This research has a great impact on the social implementation of technology because it enables the construction of low-DoF task-specific systems based on the extraction of only the features necessary for task execution by learning a complex task that appears to be high-dimensional with a high-DoF manipulator.
Yamasaki et al. (Fri,) studied this question.