Haptic robotic exploration aims to control the movements of a robot with the objective of touching an object and retrieving physical information about it. In this work, we present an innovative exploration strategy to simultaneously detect symmetries in a 3-D object and use this information to enhance shape estimation. This is achieved by leveraging a novel formulation of Gaussian process models that allows the modeling of symmetric surfaces. Our procedure does not assume any prior knowledge about the object, neither about its shape nor about the presence and type of symmetry, necessitating only an approximate estimate of the size and boundaries (bounding box). We report experimental results both in simulation and in the real world, showing that using symmetric models leads to a reduction in shape estimation error, exploration time, and in the number of physical contacts performed by a robot when exploring objects that have symmetries.

The shapes of most real-world objects are symmetric with respect to at least one plane of symmetry. This information is unconsciously used by humans when they attempt to estimate the shape of an object in presence of uncertainty or missing evidence, for example if the object is partially occluded or if they are exploring the object by touch (i.e. haptic exploration). In robotics, this concept has been used for the visual estimation of object shape. However, no attempt has been made so far to incorporate this idea into haptic-based estimation. This work presents a method for the haptic exploration of object shape that includes the assumption that a symmetry could exist. The approach combines a tailored version of Gaussian Processes and a novel exploratory procedure that is able to detect the position and orientation of any plane of symmetry. Our results show that, if one or more symmetries exist, the object shape can be estimated faster and more accurately. Interestingly, in the case that no symmetry is present, the exploration process is only slightly slower, and the final accuracy of the shape estimation is not compromised.

The lack of tactile sensation in current upper limb prostheses is the key limitation to enable more intuitive use of the prosthesis and restore the functionality of the natural limb. Electronic skin in the form of tactile sensing arrays can be integrated onto the upper limb prosthetic device to record information about touch, given back to the amputee as a sensory feedback. This contribution focusses on the development of tactile sensing arrays based on PVDF-TrFE piezoelectric polymers. Both piezoelectric polymer sensors and metal contacts have been screen-printed on a transparent plastic foil. The geometric layout and size of the 4×4 array is suitable for prosthesis fingertips. Preliminary experimental validation of the skin patches is reported in this paper.

Closing the prosthesis control loop by providing tactile sensory feedback to the user is a key point in research on active prosthetics as well as an often cited requirement of the prosthesis users. The live demo system includes: 1) electronic skin (e -skin) including a matrix of 64 sensing elements (taxels), 2) interface electronics for signal conditioning and data acquisition, 3) fully programmable multichannel electrotactile stimulator connected to flexible electrode matrices, and 4) host laptop PC which runs the online control loop implemented in Matlab.

Electronic skin can be integrated into a prosthetic device to endow the prosthesis with artificial cutaneous sensing, thereby partially restoring the sensory information lost due to an amputation. Non-invasive cutaneous electrostimulation transmits the tactile information sensed by the electronic skin on the prosthetic hand to the human brain, through the amputee's afferent nervous system. In this paper, our current benchtop prototype of a distributed sensing-stimulation system is presented, together with the envisaged high-fidelity solution which will be integrated into a real prosthetic hand.

Tactile sensing is considered a key technology for implementing complex robot interaction tasks. The contribution of this article is two-fold: (i) we propose a general-purpose algorithm for the reconstruction of deformation and force distributions for capacitance-based skin-like systems; (ii) real-time performance can be tuned according to available computational resources, which leads to an any-time formulation. Experiments (both in simulation and with real robot skin) provide a quantitative analysis of results.