Non-expert to Expert Motion Translation Using Generative Adversarial Networks

Decreasing skilled workers is a very serious problem in the world. To deal with this problem, the skill transfer from experts to robots has been researched. These methods which teach robots by human motion are called imitation learning. Experts' skills generally appear in not only position data, but also force data. Thus, position and force data need to be saved and reproduced. To realize this, a lot of research has been conducted in the framework of a motion-copying system. Recent research uses machine learning methods to generate motion commands. However, most of them could not change tasks by following human intention. Some of them can change tasks by conditional training, but the labels are limited. Thus, we propose the flexible motion translation method by using Generative Adversarial Networks. The proposed method enables users to teach robots tasks by inputting data, and skills by a trained model. We evaluated the proposed system with a 3-DOF calligraphy robot.

Involuntary Stabilization in Discrete-Event Physical Human-Robot Interaction

Robots are used by humans not only as tools but also to interactively assist and cooperate with humans, thereby forming physical human-robot interactions. In these interactions, there is a risk that a feedback loop causes unstable force interaction, in which force escalation exposes a human to danger. Previous studies have analyzed the stability of voluntary interaction but have neglected involuntary behavior in the interaction. In contrast to the previous studies, this study considered the involuntary behavior: a human's force reproduction bias for discrete-event human-robot force interaction. We derived an asymptotic stability condition based on a mathematical bias model and found that the bias asymptotically stabilizes a human's implicit equilibrium point far from the implicit equilibrium point and destabilizes the point near the point. The bias model, convergence of the interaction toward the implicit equilibrium point, and divergence around the point were consistently verified via behavioral experiments under three kinds of interactions using three different body parts: a hand finger, wrist, and foot. Our results imply that humans implicitly secure a stable and close relationship between themselves and robots with their involuntary behavior.