Estimating Tactile Models of Heterogeneous Deformable Objects in Real Time
Shaoxiong Yao, Kris Hauser
Abstract
This paper introduces a method for learning the force response of heterogeneous, deformable objects directly from robot sensor data without prior knowledge. The method estimates an object's force response given robot force or torque measurements using a novel volumetric stiffness field representation and point-based contact simulator. The stiffness of each point colliding with the robot is estimated independently and is updated upon each observed measurement using a projected diagonal Kalman filter. Experiments show that this method can update a stiffness field over 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> points at 23 Hz or higher, and is more accurate than learning-based methods in predicting torque response while touching artificial plants. The method can also be augmented with visual information to help extrapolate stiffness fields to distant parts of the touched object using only a small number of touches.