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Interpreting and predicting tactile signals for the SynTouch BioTac
The International Journal of Robotics Research ( IF 7.5 ) Pub Date : 2021-11-26 , DOI: 10.1177/02783649211047634
Yashraj S. Narang 1 , Balakumar Sundaralingam 1 , Karl Van Wyk 1 , Arsalan Mousavian 1 , Dieter Fox 1, 2
Affiliation  

In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically used to provide low-density contact information, such as contact location, center of pressure, and net force. Although useful, these data do not convey or leverage the rich information content that some tactile sensors naturally measure. This research extends robotic tactile sensing beyond reduced-order models through (1) the automated creation of a precise experimental tactile dataset for the BioTac over a diverse range of physical interactions, (2) a 3D finite-element (FE) model of the BioTac, which complements the experimental dataset with high-density, distributed contact data, (3) neural-network-based mappings from raw BioTac signals to not only low-dimensional experimental data, but also high-density FE deformation fields, and (4) mappings from the FE deformation fields to the raw signals themselves. The high-density data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible. Datasets, CAD files for the experimental testbed, FE model files, and videos are available at https://sites.google.com/nvidia.com/tactiledata.



中文翻译:

解释和预测 SynTouch BioTac 的触觉信号

在人类手中,传入神经元提供的高密度接触信息对于许多人类的抓取和操作能力至关重要。相比之下,机器人触觉传感器,包括最先进的 SynTouch BioTac,通常用于提供低密度接触信息,例如接触位置、压力中心和合力。尽管有用,但这些数据并未传达或利用某些触觉传感器自然测量的丰富信息内容。这项研究通过 (1) 在各种物理相互作用中为 BioTac 自动创建精确的实验触觉数据集,(2) BioTac 的 3D 有限元 (FE) 模型,将机器人触觉传感扩展到降阶模型之外,它用高密度、分布式的接触数据补充了实验数据集,(3) 基于神经网络的从原始 BioTac 信号到低维实验数据以及高密度 FE 变形场的映射,以及 (4) 从 FE 变形场到原始信号本身的映射。与以前可访问的相比,高密度数据流可以为抓取和操作算法提供更多数量的可解释信息。数据集、实验测试台的 CAD 文件、有限元模型文件和视频可在 https://sites.google.com/nvidia.com/tactiledata 获得。与以前可访问的相比,高密度数据流可以为抓取和操作算法提供更多数量的可解释信息。数据集、实验测试台的 CAD 文件、有限元模型文件和视频可在 https://sites.google.com/nvidia.com/tactiledata 获得。与以前可访问的相比,高密度数据流可以为抓取和操作算法提供更多数量的可解释信息。数据集、实验测试台的 CAD 文件、有限元模型文件和视频可在 https://sites.google.com/nvidia.com/tactiledata 获得。

更新日期:2021-11-27
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