人間型ロボットの触覚制御に関する研究

仿人机器人触觉控制研究

• 批准号: 11F01759
• 负责人: 菅野 重樹
• 金额: $ 1.28万
• 依托单位: Waseda University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2011
• 资助国家: 日本
• 起止时间: 2011 至 2013
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11F01759/
• 关键词: ヒューマノイドロボット; 触覚; 巧緻性; 把持; マニピュレーション; コンプライアンス; 学習; 予測モデル; 巧緻マニピュレーション; リーチング; 人間ロボットインタラクション; 家庭用ロボット

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.

正如我们在申请 JSPS 博士后奖学金的“工作包 3：触觉对象识别”中指出的那样，虽然过去已经提出了通过触觉传感进行对象识别的有趣工作，但存在局限性：通常对象是固定的，使它们在探索过程中不移动，并且朝向某个方向；经常使用夹子，而不是灵巧的手；如果使用分布式压力数据，则它来自平面触觉阵列。 TWENDY-ONE 的手提供比任何其他机器人手更丰富的触觉数据。它不仅在大部分手上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，还包括电机角度和弹簧位移等躯体传感器。我们使用多指手和正常的抓握动作来进行触觉物体识别。物体被允许（并且实际上是期望的）在抓取动作之间移动。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，深度学习取得了可喜的成果。然而，深度学习很少用于机器人技术，据我们所知，深度学习从未用于触觉传感，可能是因为很难用触觉传感器收集大量样本。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，这是触觉物体识别有史以来最具挑战性的一组。我们的结果表明，与传统神经网络相比，使用去噪自动编码器有明显的改进。我们的识别率达到了 88% 左右。这是迄今为止报告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。该成果也已提交至IEEE/RSJ国际智能机器人与系统会议（IROS）2014年会议。