5 accepted papers
The use of machine learning to investigate grasp affordances has received extensive attention over the past several decades. The existing literature provides a robust basis to build upon, though a number of aspects may be improved. Results commonly work in terms of grasp configuration, with little c
This paper describes the hardware implementation and characterization of a capacitive sensor designed to support detection and localization of nearby objects. The sensor can be mounted on the exterior of any given robotic system. The technology is particularly well-suited to detection of capacitive…
We propose a new approach to investigate and quantify dynamic grasp performance. Oftentimes, existing approaches to grasp analysis assess a grasp’s quality in a static situation. We build upon such considerations to also account for the dynamic nature of most grasp operations. In particular, these t…
By manipulation of electric fields, it is possible to detect the presence of foreign objects underwater. The presented work builds upon a previous result, in which was developed a neural network-based methodology allowing to address this detection problem for spherical objects. Hereafter, we show th
Navigation of cluttered underwater environments remains to this day a challenging task in mobile robotics. Applying an electric field to a mobile robot's direct environment and measuring perturbations of this field, one is able to detect the presence of foreign objects in close proximity of the syst…