用GAN实现非专家动作到专家级动作的灵活转换
Non-expert to Expert Motion Translation Using Generative Adversarial Networks
- 基于生成对抗网络构建动作翻译框架
- 在3自由度书法机器人上实现动作技能迁移
- 支持用户输入数据与模型训练结合教学
全球范围内熟练技工短缺问题日益严重。为应对这一挑战，通过人类动作向机器人传递技能的研究逐渐兴起，这类方法称为模仿学习。专家技能不仅体现在位置数据上，还包含力觉信息，因此需同时保存并复现位置与力数据。为此，已有大量研究基于运动复制系统开展。近期工作采用机器学习生成运动指令，但多数无法根据人类意图灵活切换任务；部分方法虽可通过条件训练实现任务变更，但标签种类受限。为此，本文提出一种基于生成对抗网络（GAN）的柔性动作翻译方法，使用户可通过输入数据与训练好的模型共同指导机器人完成任务。我们在一个3-自由度书法机器人平台上对所提系统进行了评估。
原文摘要 · Abstract (English)
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.
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