TY - GEN
T1 - Estimating Tactile Models of Heterogeneous Deformable Objects in Real Time
AU - Yao, Shaoxiong
AU - Hauser, Kris
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 105 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.
AB - 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 105 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.
UR - http://www.scopus.com/inward/record.url?scp=85165708114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165708114&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10160731
DO - 10.1109/ICRA48891.2023.10160731
M3 - Conference contribution
AN - SCOPUS:85165708114
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 12583
EP - 12589
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Y2 - 29 May 2023 through 2 June 2023
ER -