Various robotic tool manipulation methods have been developed so far. However, to our knowledge, none of them have taken into account the fact that the grasping state such as grasping position and tool angle can change at any time during the tool manipulation. In addition, there are few studies that can handle deformable tools. In this study, we develop a method for estimating the position of a tool-tip, controlling the tool-tip, and handling online adaptation to changes in the relationship between the body and the tool, using a neural network including parametric bias. We demonstrate the effectiveness of our method for online change in grasping state and for deformable tools, in experiments using two different types of robots: axis-driven robot PR2 and tendon-driven robot MusashiLarm.

翻译：迄今为止，已开发出多种机器人工具操作方法。然而，据我们所知，现有方法均未考虑到工具操作过程中抓握位置和工具角度等抓握状态可能随时发生变化的事实。此外，能够处理可变形工具的研究也较为有限。在本研究中，我们开发了一种方法，利用包含参数偏置的神经网络，实现工具尖端位置估计、工具尖端控制，并处理本体与工具之间关系的在线自适应调整。我们通过使用两种不同类型的机器人——轴驱动机器人PR2和肌腱驱动机器人MusashiLarm——进行实验，验证了所提方法在处理抓握状态在线变化及可变形工具方面的有效性。