Responsive fingers — capacitive sensing during object manipulation

Abstract

We present a novel approach of active object categorization based on an iterative Bayesian method using capacitive sensing during object manipulation. The approach uses a novel type of capacitive sensor, which can measure internal properties of materials that are inaccessible to vision or tactile sensing. The electrodes of this capacitive sensor are sufficiently flexible and thin to be attached to various types of robot tools, including humanoid fingers and palms. They are mechanically robust and wear resistant. In comparison to earlier capacitive sensors systems, we perform single ended and differential measurements over an array of electrodes, and coordinate measurement with robot manipulation to extract information not available from static measurements. We demonstrate the capability of our active object categorization system in the James robot bartender system. This system can manipulate objects and measure continuously in order to categorize empty and non-empty bottles. The principle of capacitive sensing during manipulation can be applied to more general object manipulation tasks in robotics and also in other fields of industrial automation.