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首页> 外文会议>Joint IEEE International Conference on Development and Learning and Epigenetic Robotics >Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto
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Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto

机译：在仿人机器人Affetto上转移复杂运动技能的学习

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摘要

Although autonomous robots can perform particularly well at highly specific tasks, learning each task in isolation is a very costly process, not only in terms of time but also in terms of hardware wearout and energy usage. Hence, robotic systems need to be able to adapt quickly to new situations in order to be useful in everyday tasks. One way to address this issue is transfer learning, which aims at reusing knowledge obtained in one situation, in a new related one. In this contribution, we develop a drumming scenario with the child robot Affetto where the environment changes such that the scene can only be observed through a mirror. In order to address such domain adaptation problems, we propose a novel transfer learning algorithm that aims at mapping data from the new domain in such a way that the original model is applicable again. We demonstrate this method on an artificial data set as well as in the robot setting.
机译：尽管自主机器人在高度特定的任务上表现特别出色，但孤立地学习每个任务是一个非常昂贵的过程，不仅在时间上，而且在硬件损耗和能源使用方面。因此，为了在日常任务中有用，机器人系统需要能够快速适应新情况。解决这一问题的一种方法是转移学习，其目的是在一种新的相关情况下重用在一种情况下获得的知识。在这项贡献中，我们与儿童机器人Affetto一起开发了击鼓场景，环境发生了变化，因此只能通过镜子才能观察到场景。为了解决这样的领域适应问题，我们提出了一种新颖的转移学习算法，该算法旨在以一种可以使原始模型再次适用的方式映射来自新领域的数据。我们在人工数据集以及机器人设置中演示了这种方法。

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