Robotic Kinesthesia: Estimating Object Geometry and Material With Robot's Haptic Senses

Abstract

Humans excel at determining the shape and material of objects through touch. Drawing inspiration from this ability, we propose a robotic system that incorporates haptic sensing capability into its artificial recognition system to jointly learn the shape and material types of an object. To achieve this, we employ a serially connected robotic arm and develop a supervised learning task that learns and classifies target surface geometry and material types using multivariate time-series data from joint torque sensors. Additionally, we propose a joint torque-to-position generation task to derive a one-dimensional surface profile based on torque measurements. Experimental results successfully validate the proposed torque-based classification and regression tasks, suggesting that a robotic system can employ haptic sensing (i.e., perceived force) from each joint to recognize material types and geometry, akin to human abilities.