  Collaborative robots are expected to physically interact with humans in daily
living and workplace, including industrial and healthcare settings. A related
key enabling technology is tactile sensing, which currently requires addressing
the outstanding scientific challenge to simultaneously detect contact location
and intensity by means of soft conformable artificial skins adapting over large
areas to the complex curved geometries of robot embodiments. In this work, the
development of a large-area sensitive soft skin with a curved geometry is
presented, allowing for robot total-body coverage through modular patches. The
biomimetic skin consists of a soft polymeric matrix, resembling a human
forearm, embedded with photonic Fiber Bragg Grating (FBG) transducers, which
partially mimics Ruffini mechanoreceptor functionality with diffuse,
overlapping receptive fields. A Convolutional Neural Network deep learning
algorithm and a multigrid Neuron Integration Process were implemented to decode
the FBG sensor outputs for inferring contact force magnitude and localization
through the skin surface. Results achieved 35 mN (IQR = 56 mN) and 3.2 mm (IQR
= 2.3 mm) median errors, for force and localization predictions, respectively.
Demonstrations with an anthropomorphic arm pave the way towards AI-based
integrated skins enabling safe human-robot cooperation via machine
intelligence.
