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High-density afferents in the human hand have long been regarded as essentialfor human grasping and manipulation abilities. In contrast, robotic tactilesensors are typically used to provide low-density contact data, such ascenter-of-pressure and resultant force. Although useful, this data does notexploit the rich information content that some tactile sensors (e.g., theSynTouch BioTac) naturally provide. This research extends robotic tactilesensing beyond reduced-order models through 1) the automated creation of aprecise tactile dataset for the BioTac over diverse physical interactions, 2) a3D finite element (FE) model of the BioTac, which complements the experimentaldataset with high-resolution, distributed contact data, and 3)neural-network-based mappings from raw BioTac signals to low-dimensionalexperimental data, and more importantly, high-density FE deformation fields.These data streams can provide a far greater quantity of interpretableinformation for grasping and manipulation algorithms than previouslyaccessible.

Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework