TACMAN addresses the key problem of developing an information processing and control technology enabling robot hands to exploit tactile sensitivity and thus become as dexterous as human hands. The current availability of the required technology now allows us to considerably advance in-hand manipulation. TACMAN’s goal is to develop fundamentally new approaches which can replace manual labor under inhumane conditions by endowing robots with such tactile manipulation abilities, by transferring insights from human neuroscientific studies into machine learning algorithms.
TACMAN will provide an innovative new technology that is key for bringing industrial manufacturing back to Europe. Consider the case of the iPhone, where most mechanical manipulation of the major components is achieved by manual human labour under terrible work conditions and not by advanced industrial robots—despite that millions of iPhones are industrially assembled per month. The reason for this absence of appropriate automation is the lack of manipulation skills of current robots.
Commercially available robotic hand-arm systems move more accurately and faster than humans, and their sensors see more and at a higher precision—even the smallest forces and torques can be detected. Despite these impressive sensorimotor abilities, current robots are terrible at manipulation when compared to humans. Neuroscience provides a clear reason for the superiority of human hands: During manipulation, humans make substantial use of the data from tactile sensors, i.e., the information obtained through the feeling in the human’s fingers. Robot hands are lacking this key ability! Hence, the rationale of TACMAN is that this performance gap in manipulation ability can be filled by
TACMAN aims to integrate the most robust available tactile sensors into the control of existing modern robot hands, and, based on this control law, develop tactile sensor-based manipulation solutions. To make this innovation tractable in a three year project, we aim only on recognising and handling objects that are already in the hand. The structure of the project is designed to allow quick scaling from straightforward, well-captured scenarios employing a single finger to complex multi-fingered manipulation.