  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.
