Fast In-Hand Slip Control on Unfeatured Objects with Programmable Tactile Sensing

Accurate dynamic object manipulation in a robotic hand remains a difficult task, especially when frictional slip is involved. Prior solutions involve extensive data collection to train complex models to control the hand that do not necessarily general- ize to other slip circumstances. Our approach focuses on direct slip sensing using a tactile sensor with a capacitive array, coupled with a programmable system on a chip, capable of mode switching and sampling rate adjustment. We characterize the sensor’s capacity to sense slip features at higher speeds and introduce a novel methodol- ogy for estimating motions. Low-level sensor reprogramming that couples multiple taxels improves slip avoidance and reaction time during rapid slip onset events. The technology also tracks domi- nant surface vibration frequencies resulting from stick-slip cycles, estimating speed and acceleration of smooth flat surfaces. Using a parallel-jaw robotic gripper, we demonstrate dynamic reposition- ing of objects lacking trackable surface features within the hand. The goal of this investigation is to support faster reasoning and reflexes for dynamic dexterous robots that experience directional in-hand slip.