用皮肤传感器捕捉触觉动作，让机器人通过触摸方式实现复杂操作。
Skin-Machine Interface with Multimodal Contact Motion Classifier
- 用循环神经网络学习多模态触觉时序数据，识别操作者不同触碰动作。
- 分类准确率超95%，使双臂移动机械臂能执行多样化任务。
- 适合人机交互、柔性传感与智能机器人控制方向的研究者参考。
本文提出一种新型框架，利用皮肤传感器作为复杂机器人的新型操作界面。所采用的皮肤传感器可在多个接触点量化多模态触觉信息。传感器生成的时序数据有望用于分类操作者表现出的不同接触动作。通过将分类结果映射至机器人运动基元，改变触碰方式即可生成多样化的机器人动作。本文聚焦基于学习的接触动作分类器，采用循环神经网络，是该框架成功的关键。同时阐明软硬件设计所需条件：首先，多模态感知及其综合编码显著提升分类准确率与学习稳定性，同时使用所有模态输入效果最佳；其次，将皮肤传感器安装于柔性柔顺支撑上，可激活三轴加速度计，测量水平触觉信息，增强与其他模态的相关性，并吸收机器人运行中产生的噪声。基于上述设计，分类器准确率超过95%，使双臂移动操作机械臂可通过皮肤-机器接口完成多样化任务。
原文摘要 · Abstract (English)
This paper proposes a novel framework for utilizing skin sensors as a new operation interface of complex robots. The skin sensors employed in this study possess the capability to quantify multimodal tactile information at multiple contact points. The time-series data generated from these sensors is anticipated to facilitate the classification of diverse contact motions exhibited by an operator. By mapping the classification results with robot motion primitives, a diverse range of robot motions can be generated by altering the manner in which the skin sensors are interacted with. In this paper, we focus on a learning-based contact motion classifier employing recurrent neural networks. This classifier is a pivotal factor in the success of this framework. Furthermore, we elucidate the requisite conditions for software-hardware designs. Firstly, multimodal sensing and its comprehensive encoding significantly contribute to the enhancement of classification accuracy and learning stability. Utilizing all modalities simultaneously as inputs to the classifier proves to be an effective approach. Secondly, it is essential to mount the skin sensors on a flexible and compliant support to enable the activation of three-axis accelerometers. These accelerometers are capable of measuring horizontal tactile information, thereby enhancing the correlation with other modalities. Furthermore, they serve to absorb the noises generated by the robot's movements during deployment. Through these discoveries, the accuracy of the developed classifier surpassed 95 %, enabling the dual-arm mobile manipulator to execute a diverse range of tasks via the Skin-Machine Interface. https://youtu.be/UjUXT4Z4BC8
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