ShadowSense: Detecting Human Touch in a Social Robot Using Shadow Image Classification
ShadowSense: Detecting Human Touch in a Social Robot Using Shadow Image Classification
复制标题
ShadowSense：使用阴影图像分类检测社交机器人中的人体触摸
DOI：
10.1145/3432202
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hoffman, Guy
中科院分区：
文献类型：
--
作者：
Hu, Yuhan;Bejarano, Sara Maria;Hoffman, Guy
本文提出并评估了图像分类在详细的全身人机触觉交互中的应用。位于半透明机器人皮肤下方的摄像头捕捉人类触摸产生的阴影，并从捕捉到的图像推断社交手势。这种方法可以与机器人进行丰富的触觉交互，而不需要传统社交机器人触觉皮肤中使用的传感器阵列。它还支持与非刚性机器人的触摸交互，实现对不同尺寸和表面形状的机器人的高分辨率传感，并且消除了与机器人直接接触的要求。我们通过充气机器人和独立测试设备、使用密集连接卷积网络从阴影中识别触摸手势的算法以及跟踪触摸和悬停阴影位置的算法来演示这个想法。我们的实验表明，该系统可以在三种照明条件下以 87.5 - 96.0% 的准确度区分 6 个触摸手势（具体取决于照明），并且可以准确跟踪触摸位置以及推断现实交互条件下的运动活动。该方法的其他应用包括充气机器人和家庭隐私维护机器人上的交互式屏幕。
This paper proposes and evaluates the use of image classification for detailed, full-body human-robot tactile interaction. A camera positioned below a translucent robot skin captures shadows generated from human touch and infers social gestures from the captured images. This approach enables rich tactile interaction with robots without the need for the sensor arrays used in traditional social robot tactile skins. It also supports the use of touch interaction with non-rigid robots, achieves high-resolution sensing for robots with different sizes and shape of surfaces, and removes the requirement of direct contact with the robot. We demonstrate the idea with an inflatable robot and a standing-alone testing device, an algorithm for recognizing touch gestures from shadows that uses Densely Connected Convolutional Networks, and an algorithm for tracking positions of touch and hovering shadows. Our experiments show that the system can distinguish between six touch gestures under three lighting conditions with 87.5 - 96.0% accuracy, depending on the lighting, and can accurately track touch positions as well as infer motion activities in realistic interaction conditions. Additional applications for this method include interactive screens on inflatable robots and privacy-maintaining robots for the home.