Perception and Handling enabled by Artificial tactile SEnsing

The sense of touch is the sense that captures the mechanical interaction with our surroundings. When we enter in contact with an object, the mechanical deformation of our skin and the resistance opposed to our limb, inform the central nervous system about the contact condition. From there information such as the weight, center of gravity, texture and slipperiness of the object are extracted. These tactile percepts advise the planification of motor commands in order to achieve the mesmerizing dexterity of the human hand. Regardless of the obvious benefit of relying on touch system, current commercial robotic systems are rarely equipped with tactile sensors that capture relevant information about the tactile scene. Instead they mostly depend on vision systems to perform tasks. Many reasons motivate this technological preference. First, contrary to cameras, robotic fingers and artificial skins with enough resolution and robustness are not yet broadly available to researchers and industrials. Secondly, even the best artificial sensing systems lack framework for processing and recognizing the tactile scene. The few studies that did tackle these issues are often linked to image processing and often neglect to include frictional and adhesion properties, essential for swift control of robotic hand and surface texture characterization. This research program aims at bridging the current limitation of soft-sensor design and computational touch to bring the sense of touch to a wide variety of robotic applications. The sensor will leverage the recent advances in soft material construction to build an artificial fingertip that can match the perceptual capability and mechanical strength of their human counterpart. Data provided by sensors will be use to infer the state of contact via a physically motivated computational framework that is based on tribology and contact mechanics. Research on touch is still in its infancy compared to visual and auditory perception. Building a physically-grounded framework around artificial touch has the same innovative potential as computer vision had 30 years ago.