Improving Grasp Performance Using In-Hand Proximity and Dynamic Tactile Sensing

Abstract

We demonstrate how low-cost in-hand proximity and dynamic tactile sensing can dramatically improve the reliability of basic manipulation tasks. We use an array of infrared proximity sensors embedded in a transparent elastic polymer and an accelerometer in the robot’s wrist to extract proximity and dynamic tactile information that is inspired by the mechanoreceptors in the human skin. We break the manipulation task down into eight distinct phases and show (1) how proximity information can be used to improve reliability of picking and placing objects, and (2) how dynamic tactile information can be used to discern different phases of grasping. We present experimental results using a Baxter robot involved in a tower construction task.