Interpreting and Predicting Tactile Signals for the SynTouch BioTac

TL;DR

This paper develops methods to interpret and predict high-density tactile signals from the SynTouch BioTac sensor, combining experimental data, finite element modeling, and neural networks to enhance robotic grasping capabilities.

Contribution

It introduces an integrated approach using experimental datasets, FE modeling, and neural networks to extract richer tactile information from the BioTac sensor.

Findings

Created a precise tactile dataset for BioTac across diverse interactions.

Developed neural networks mapping raw signals to high-density FE deformation data.

Demonstrated the potential for richer tactile information to improve manipulation algorithms.

Abstract

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.