论文标题
通过基于物理和数据驱动的框架来解释和预测触觉信号
Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework
论文作者
论文摘要
长期以来，人类手中的高密度传入对于人类的抓握和操纵能力至关重要。相比之下，机器人触觉传感器通常用于提供低密度接触数据，例如压力中心和产量。尽管有用，但这些数据并不能利用某些触觉传感器（例如Syntouch Biotac）自然提供的丰富信息内容。 This research extends robotic tactile sensing beyond reduced-order models through 1) the automated creation of a precise tactile dataset for the BioTac over diverse physical interactions, 2) a 3D finite element (FE) model of the BioTac, which complements the experimental dataset with high-resolution, distributed contact data, and 3) neural-network-based mappings from raw BioTac signals to low-dimensional experimental data, and more importantly,高密度FE变形场。这些数据流可以提供比以前可访问的更大数量的可解释信息来抓握和操纵算法。
High-density afferents in the human hand have long been regarded as essential for human grasping and manipulation abilities. In contrast, robotic tactile sensors are typically used to provide low-density contact data, such as center-of-pressure and resultant force. Although useful, this data does not exploit the rich information content that some tactile sensors (e.g., the SynTouch BioTac) naturally provide. This research extends robotic tactile sensing beyond reduced-order models through 1) the automated creation of a precise tactile dataset for the BioTac over diverse physical interactions, 2) a 3D finite element (FE) model of the BioTac, which complements the experimental dataset with high-resolution, distributed contact data, and 3) neural-network-based mappings from raw BioTac signals to low-dimensional experimental data, and more importantly, high-density FE deformation fields. These data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible.
