Collaborative robots are expected to physically interact with humans in daily living and the workplace, including industrial and healthcare settings. A key related 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 fibre Bragg grating 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 fibre Bragg grating sensor outputs for inference of contact force magnitude and localization through the skin surface. Results of 35 mN (interquartile range 56 mN) and 3.2 mm (interquartile range 2.3 mm) median errors were achieved for force and localization predictions, respectively. Demonstrations with an anthropomorphic arm pave the way towards artificial intelligence based integrated skins enabling safe human–robot cooperation via machine intelligence.

Soft robots hold promise for well-matched interactions with delicate objects, humans and unstructured environments owing to their intrinsic material compliance. Movement and stiffness modulation, which is challenging yet needed for an effective demonstration, can be devised by drawing inspiration from plants. Plants use a coordinated and reversible modulation of intracellular turgor (pressure) to tune their stiffness and achieve macroscopic movements. Plant-inspired osmotic actuation was recently proposed, yet reversibility is still an open issue hampering its implementation, also in soft robotics. Here we show a reversible osmotic actuation strategy based on the electrosorption of ions on flexible porous carbon electrodes driven at low input voltages (1.3 V). We demonstrate reversible stiffening (~5-fold increase) and actuation (~500 deg rotation) of a tendril-like soft robot (diameter ~1 mm). Our approach highlights the potential of plant-inspired technologies for developing soft robots based on biocompatible materials and safe voltages making them appealing for prospective applications.

This study addresses a design and calibration methodology based on numerical finite element method (FEM) modeling for the development of a soft tactile sensor able to simultaneously solve the magnitude and the application location of a normal load exerted onto its surface. The sensor entails the integration of a Bragg grating fiber optic sensor in a Dragon Skin 10 polymer brick (110 mm length, 24 mm width). The soft polymer mediates the transmission of the applied load to the buried fiber Bragg gratings (FBGs), and we also investigated the effect of sensor thickness on receptive field and sensitivity, both with the developed model and experimentally. Force-controlled indentations of the sensor (up to 2.5 N) were carried out through a cylindrical probe applied along the direction of the optical fiber (over an ∼90 mm span in length). A finite element model of the sensor was built and experimentally validated for 1 and 6 mm thicknesses of the soft polymeric encapsulation material, considering that the latter thickness resulted from numerical simulations as leading to optimal cross talk and sensitivity, given the chosen soft material. The FEM model was also used to train a neural network so as to obtain the inverse sensor function. Using four FBG transducers embedded in the 6-mm-thick soft polymer, the proposed machine learning approach managed to accurately detect both load magnitude ( R = 0.97) and location ( R = 0.99) over the whole experimental range. The proposed system could be used for developing tactile sensors that can be effectively used for a broad range of applications.

Oxidative stress represents a common issue in most neurological diseases, causing severe impairments of neuronal cell physiological activity that ultimately lead to neuron loss of function and cellular death. In this work, lipid-coated polydopamine nanoparticles (L-PDNPs) are proposed both as antioxidant and neuroprotective agents, and as well as a photo-thermal conversion platform able to stimulate neuronal activity. L-PDNPs showed the ability to counteract reactive oxygen species (ROS) accumulation in differentiated SH-SY5Y, prevented mitochondrial ROS-induced dysfunctions, and stimulated neurite outgrowth. Moreover, for the first time in the literature, the photo-thermal conversion capacity of L-PDNPs was used to increase the intracellular temperature of neuron-like cells through near-infrared (NIR) laser stimulation, and this phenomenon was thoroughly investigated using a fluorescent temperature-sensitive dye and modeled from a mathematical point of view. It was also demonstrated that the increment in temperature caused by the NIR stimulation of L-PDNPs was able to produce a Ca2+ influx in differentiated SH-SY5Y, being, to the best of our knowledge, the first example of organic nanostructures used in such an approach. This work could pave the way to new and exciting applications of polydopamine-based and of other NIR-responsive antioxidant nanomaterials in neuronal research.

Polydopamine (PDA) is a polymer that derives from the self-polymerization of the biomolecule dopamine. It can be easily synthesized to obtain spherical nanoparticles (PDNPs), tunable in terms of size, loaded cargo, and surface functionalization. PDNPs have been increasingly attracting the attention of the research community due to their elevated versatility in the biomedicine field, for their excellent ability to encapsulate drugs, to convert near-infrared (NIR) radiation into heat, and to act as an antioxidant agent. Size is an important aspect to be considered, especially concerning the specific intended field of application. This work aims at investigating how changes in the size of PDNPs affect the nanoparticle properties relevant for biomedical applications, especially focusing on cancer nanomedicine. A library of differently sized PDNPs (from 145 to 957 nm) has been obtained by varying the ammonia/dopamine molar ratio during the synthesis procedure, and detailed characterization in terms of biocompatibility, cell internalization, antioxidant capacity, and photothermal conversion has been carried out. Experiments showed that nanoparticles with a larger diameter display higher NIR absorbance, superior resistance to degradation, and higher photothermal conversion capacity (the latter confirmed by a mathematical model). On the other hand, a reduction in diameter size induces both improved antioxidant properties and enhanced cellular uptake. Herein, we provide a useful tool, allowing one to choose the proper size of PDNPs tailored for specific biomedical applications.