Publications - Conference Paper

This work proposes a representation that comprises both shape and friction, as well as the exploration strategy to gather them from an object. The representation is developed under a common probabilistic framework, particularly it uses a Gaussian Process to approximate the distribution of the friction coefficient over the surface, also represented as a Gaussian Process. The surface model is exploited to compute straight lines (geodesic flows) that guide the exploration. The exploration follows these flows by employing an impedance controller in pursuance of safety, shape accommodation and contact enforcement, while measuring the necessary data to estimate the friction coefficient. The exploratory probes consist of an RGBD camera and an Intrinsic Tactile sensor (ITs) mounted on a robotic arm. Experimental results give evidence for the effectiveness of the algorithm in the friction coefficient gathering and enrichment of the object representation.

Imagine you are pushing your finger against a deformable, compliant object. The change in the area of contact can provide an estimate of the relative displacement of the finger, such that the larger is the area of contact, the larger is the displacement. Does the human haptic system use this as a cue for estimating the displacement of the finger with respect to the external object? Here we conducted a psychophysical experiment to test this hypothesis. Participants compared the passive displacement of the index finger between a reference and a comparison stimulus. The compliance of the contacted object changed between the two stimuli, thus producing a different area-displacement relationship. In accordance with the hypothesis, the modulation of the area-displacement relationship produced a bias in the perceived displacement of the finger.