# Non-expert to Expert Motion Translation Using Generative Adversarial Networks

## TL;DR

This paper introduces a GAN-based method for translating human motion data into robot commands, enabling flexible skill transfer and task adaptation for robots, demonstrated on a 3-DOF calligraphy robot.

## Contribution

The paper presents a novel GAN framework for non-expert to expert motion translation, allowing flexible task learning without limited labels.

## Key findings

- Effective motion translation demonstrated on a 3-DOF robot.
- Enables task change by input data without extensive labeling.
- Improves skill transfer from humans to robots.

## Abstract

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.