Tool-body assimilation model considering grasping motion through deep learning
Tool-body assimilation model considering grasping motion through deep learning
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DOI：
10.1016/j.robot.2017.01.002
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
K. Takahashi;Kitae Kim;T. Ogata;S. Sugano
中科院分区：
文献类型：
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作者：
K. Takahashi;Kitae Kim;T. Ogata;S. Sugano
我们提出了一个工具体同化模型，认为掌握在电机牙牙学语使用工具。具有工具使用技能的机器人在人机共生中可能很有用，因为这允许机器人扩展其任务执行能力。过去的研究，包括工具的身体同化的方法主要集中在获得的工具的功能，并演示了机器人开始与机器人的工具预先连接到它的运动。这意味着机器人将无法决定是否以及在哪里抓住工具。在真实的生活环境中，机器人需要考虑工具抓取位置的可能性，然后抓取工具。为了解决这些问题，机器人执行电机babbling通过graspingandnongraspingthe工具，以学习机器人的身体模型和工具的功能。此外，机器人抓取工具的各个部分，以从不同的抓取位置学习不同的工具功能。运动体验是使用深度学习来学习的。在模型评估中，机器人在没有工具的情况下操纵对象任务，并且使用不同形状的几个工具。机器人在被示出初始状态和目标图像之后，通过决定是否以及在哪里抓住工具来产生运动。因此，当初始状态和目标图像被提供给机器人时，机器人能够生成正确的运动和抓取决定。
We propose a tool-body assimilation model that considers grasping during motor babbling for using tools. A robot with tool-use skills can be useful in human–robot symbiosis because this allows the robot to expand its task performing abilities. Past studies that included tool-body assimilation approaches were mainly focused on obtaining the functions of the tools, and demonstrated the robot starting its motions with a tool pre-attached to the robot. This implies that the robot would not be able to decide whether and where to grasp the tool. In real life environments, robots would need to consider the possibilities of tool-grasping positions, and then grasp the tool. To address these issues, the robot performs motor babbling bygraspingandnongraspingthe tools to learn the robot’s body model and tool functions. In addition, the robot grasps various parts of the tools to learn different tool functions from different grasping positions. The motion experiences are learned using deep learning. In model evaluation, the robot manipulates an object task without tools, and with several tools of different shapes. The robot generates motions after being shown the initial state and a target image, by deciding whether and where to grasp the tool. Therefore, the robot is capable of generating the correct motion and grasping decision when the initial state and a target image are provided to the robot.