AUTHOR=Rongala Udaya Bhaskar , Mazzoni Alberto , Chiurazzi Marcello , Camboni Domenico , Milazzo Mario , Massari Luca , Ciuti Gastone , Roccella Stefano , Dario Paolo , Oddo Calogero Maria TITLE=Tactile Decoding of Edge Orientation With Artificial Cuneate Neurons in Dynamic Conditions JOURNAL=Frontiers in Neurorobotics VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2019.00044 DOI=10.3389/fnbot.2019.00044 ISSN=1662-5218 ABSTRACT=

Generalization ability in tactile sensing for robotic manipulation is a prerequisite to effectively perform tasks in ever-changing environments. In particular, performing dynamic tactile perception is currently beyond the ability of robotic devices. A biomimetic approach to achieve this dexterity is to develop machines combining compliant robotic manipulators with neuroinspired architectures displaying computational adaptation. Here we demonstrate the feasibility of this approach for dynamic touch tasks experimented by integrating our sensing apparatus in a 6 degrees of freedom robotic arm via a soft wrist. We embodied in the system a model of spike-based neuromorphic encoding of tactile stimuli, emulating the discrimination properties of cuneate nucleus neurons based on pathways with differential delay lines. These strategies allowed the system to correctly perform a dynamic touch protocol of edge orientation recognition (ridges from 0 to 40°, with a step of 5°). Crucially, the task was robust to contact noise and was performed with high performance irrespectively of sensing conditions (sensing forces and velocities). These results are a step forward toward the development of robotic arms able to physically interact in real-world environments with tactile sensing.