论文标题
针对机器人系统的人触觉手势解释
Towards Human Haptic Gesture Interpretation for Robotic Systems
论文作者
论文摘要
人类机器人相互作用（PHRI）的效率和交流效率低，而不是人类人类的相互作用，一个关键的原因是机器人系统中缺乏信息感。解释人的触摸手势是一项细微的，具有挑战性的任务，人类和机器人能力之间的差距极大。在证明人触摸识别能力的先前作品中，传感器，手势类别，特征集和分类算法的差异产生了不可转移结果的集团，并且缺乏标准。 To address this gap, this work presents 1) four proposed touch gesture classes that cover an important subset of the gesture characteristics identified in the literature, 2) the collection of an extensive force dataset on a common pHRI robotic arm with only its internal wrist force-torque sensor, and 3) an exhaustive performance comparison of combinations of feature sets and classification algorithms on this dataset.我们在测试集中提出的手势定义中证明了高分类精度，这强调了原始数据上的神经网络工作分类器优于特征集和算法的其他组合。随附的视频在这里：https：//youtu.be/gjpvimnku68
Physical human-robot interactions (pHRI) are less efficient and communicative than human-human interactions, and a key reason is a lack of informative sense of touch in robotic systems. Interpreting human touch gestures is a nuanced, challenging task with extreme gaps between human and robot capability. Among prior works that demonstrate human touch recognition capability, differences in sensors, gesture classes, feature sets, and classification algorithms yield a conglomerate of non-transferable results and a glaring lack of a standard. To address this gap, this work presents 1) four proposed touch gesture classes that cover an important subset of the gesture characteristics identified in the literature, 2) the collection of an extensive force dataset on a common pHRI robotic arm with only its internal wrist force-torque sensor, and 3) an exhaustive performance comparison of combinations of feature sets and classification algorithms on this dataset. We demonstrate high classification accuracies among our proposed gesture definitions on a test set, emphasizing that neural net-work classifiers on the raw data outperform other combinations of feature sets and algorithms. The accompanying video is here: https://youtu.be/gJPVImNKU68
