人間型ロボットの触覚制御に関する研究

正如我们在《工作包3：触觉物体识别》中在申请JSPS博士后奖学金时指出的那样，虽然过去已经提出了通过触觉感知进行物体识别的有趣工作，但也存在局限性：通常物体是固定的，因此它们在探索过程中不会移动，并且它们是在某个方向上定位的；通常使用的是夹持器，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。Twendy-One的手比任何其他机器手都能提供更丰富的触觉数据。它不仅在大部分手部配备了分布式触觉皮肤传感器，在每个指尖配备了6轴F/T传感器，还包括马达角度和弹簧位移等体感传感器。我们已经使用这种多指手和正常的抓取动作来识别触觉对象。物体被允许(实际上也是期望的)在抓取动作之间移动。当使用触觉传感器时，还不清楚哪些特征对物体识别有用。最近，深度学习显示出了可喜的结果。然而，深度学习很少被用于机器人学，据我们所知，从未用于触觉感知，可能是因为用触觉传感器很难收集很多样本。我们使用深度学习技术来识别触觉对象。机器人必须识别20个不同的物体，这是有史以来用于触觉物体识别的最具挑战性的一组。我们的结果表明，与传统的神经网络相比，使用去噪自动编码器有明显的改善。我们达到了88%左右的识别率。这是迄今为止报道的识别相对于手的方向和平移未知的抓取物体的最高识别率之一。研究结果也已提交给IEEE/RSJ智能机器人和系统国际会议(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.