Assist-as-needed robotic trainer based on reinforcement learning and its application to dart-throwing.

This paper proposes a novel robotic trainer for motor skill learning. It is user-adaptive inspired by the assist-as-needed principle well known in the field of physical therapy. Most previous studies in the field of the robotic assistance of motor skill learning have used predetermined desired trajectories, and it has not been examined intensively whether these trajectories were optimal for each user.

Throwing darts utilizes the interaction torque of the elbow joint.

Acquiring the skillful movements of experts is a difficult task in many fields. If we find quantitative indices of skillful movement, we can develop an adaptive training system using the indices. We focused on throwing darts in our previous study.

Comparison of experts and non-experts in throwing darts based on optimization criteria.

Acquiring skillful movements of experts is a difficult task in many fields. Since non-experts often fail to find out how to improve their skill, it is desirable to find quantitative indices of skillful movements that clarify the difference between experts and non-experts. If we find quantitative indices, we can develop an adaptive training system using the indices.