基于图像深度学习的柔性关节机器人动态运动生成

Yuheng Wu, K. Takahashi, H. Yamada, Kitae Kim, Shingo Murata, S. Sugano, T. Ogata
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引用次数: 1

摘要

具有柔性关节的机器人由于能够被动地适应环境变化,利用惯性实现动态运动,近年来受到了研究人员的关注。在以往的研究中,提出了利用深度学习获取身体模型的方法,实现了动态运动学习。然而,使用末端执行器位置作为视觉反馈信号来训练机器人,将机器人只能知道任务与自身之间的关系,而不能知道环境与自身之间的关系。在本研究中,我们建议使用图像作为反馈信号,使机器人能够在任务环境中感知全局。这种动作学习是通过使用原始图像数据的深度学习来完成的。在实验中,我们让机器人进行一次任务运动来获取运动和图像数据。然后,我们使用卷积自编码器从原始图像数据中提取图像特征。将提取的图像特征与运动数据相结合,训练递归神经网络。因此,通过对图像数据的深度学习进行运动学习,使机器人能够获取环境信息并执行需要考虑环境变化的任务,利用其被动适应的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Motion Generation by Flexible-Joint Robot based on Deep Learning using Images
Robots with flexible joints have recently been attracting attention from researchers because such robots can passively adapt to environmental changes and realize dynamic motion that uses inertia. In previous research, body-model acquisition using deep learning was proposed and dynamic motion learning was achieved. However, using the end-effector position as a visual feedback signal to train a robot limits what the robot can know to only the relation between the task and itself, instead of the relation between the environment and itself. In this research, we propose to use images as a feedback signal so that the robot can have a sense of the overall situation within the task environment. This motion learning is performed via deep learning using raw image data. In an experiment, we let a robot perform task motions once to acquire motor and image data. Then, we used a convolutional auto-encoder to extract image features from raw image data. The extracted image features were used in combination with motor data to train a recurrent neural network. As a result, motion learning through deep learning from image data allowed the robot to acquire environmental information and conduct tasks that require consideration of environmental changes, making use of its advantage of passive adaptation.
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