人間型ロボットの触覚制御に関する研究

正如我们在申请JSPS博士后时在“Work package 3: Tactile object recognition”中指出的那样，虽然过去已经有一些关于通过触觉感知进行物体识别的有趣工作，但存在局限性：通常物体是固定的，因此在探索过程中它们不会移动，并且它们朝向一定的方向；通常用的是钳子，而不是灵巧的手；如果使用分布式压力数据，它来自平面触觉阵列。与其他机器人手相比，twenty - one的手提供了更丰富的触觉数据。它不仅配备了分布在大部分手部的触觉皮肤传感器和每个指尖的6轴F/T传感器，还包括电机角度和弹簧位移等躯体传感器。我们已经使用了多指手和正常的抓取动作来进行触觉对象识别。对象被允许（并且确实被期望）在抓取动作之间移动。当使用触觉传感器时，尚不清楚哪些特征对物体识别有用。最近，深度学习已经显示出有希望的结果。然而，深度学习很少用于机器人，据我们所知也从未用于触觉传感，可能是因为使用触觉传感器很难收集大量样本。我们采用深度学习技术进行触觉物体识别。机器人必须识别20个不同的物体，这是触觉物体识别中最具挑战性的一组。我们的研究结果表明，与传统的神经网络相比，使用去噪自编码器有明显的改进。我们的识别率达到了88%左右。这是迄今为止报道的最高识别率之一，用于识别未知方向和相对于手的平移的抓取物体。研究结果也已提交给IEEE/RSJ智能机器人与系统国际会议（IROS） 2014年会议。

As we have pointed out in "Work package 3 : Tactile object recognition" in the application for the JSPS postdoctoral fellowship, while interesting work on object recognition through tactile sensing has been presented in the past, there are limitations : usually the objects are fixated, so that they do not move during exploration, and they are oriented in a certain direction ; often grippers, and not dexterous hands are used ; if distributed pressure data is used, it comes from flat tactile arrays. The hands of TWENDY-ONE provide richer tactile data than any other robotic hand. It is equipped not only with distributed tactile skin sensors on most of the hand and 6-axis F/T sensors in each fingertip, but includes also somatic sensors such as motor angles and spring displacements. We have used that multifingered hand and normal grasping actions for tactile object recognition. The objects are allowed (and indeed expected) to move between grasping actions. When using tactile sensors, it is not clear what kinds of features are useful for object recognition. Recently, deep learning has shown promising results. Nevertheless, deep learning has rarely been used in robotics and to our best knowledge never for tactile sensing, probably because it is difficult to gather many samples with tactile sensors. We have employed deep learning techniques for tactile object recognition. The robot had to identify 20 different objects, the most challenging set ever used for tactile object recognition. Our results show a clear improvement when using a denoising autoencoder compared to traditional neural networks. We achieved a recognition rate of about 88%. This is one of the highest recognition rates reported so far for recognizing grasped objects with unknown orientation and translation relative to the hand. The results have also been submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2014 conference.