TY - GEN
T1 - Virtual Haptic System for Shape Recognition Based on Local Curvatures
AU - Garrofé, Guillem
AU - Parés, Carlota
AU - Gutiérrez, Anna
AU - Ruiz, Conrado
AU - Serra, Gerard
AU - Miralles, David
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Haptic object recognition is widely used in various robotic manipulation tasks. Using the shape features obtained at either a local or global scale, robotic systems can identify objects solely by touch. Most of the existing work on haptic systems either utilizes a robotic arm with end-effectors to identify the shape of an object based on contact points, or uses a surface capable of recording pressure patterns. In this work, we introduce a novel haptic capture system based on the local curvature of an object. We present a haptic sensor system comprising of three aligned and equally spaced fingers that move towards the surface of an object at the same speed. When an object is touched, our system records the relative times between each contact sensor. Simulating our approach in a virtual environment, we show that this new local and low-dimensional geometrical feature can be effectively used for shape recognition. Even with 10 samples, our system achieves an accuracy of over 90 % without using any sampling strategy or any associated spatial information.
AB - Haptic object recognition is widely used in various robotic manipulation tasks. Using the shape features obtained at either a local or global scale, robotic systems can identify objects solely by touch. Most of the existing work on haptic systems either utilizes a robotic arm with end-effectors to identify the shape of an object based on contact points, or uses a surface capable of recording pressure patterns. In this work, we introduce a novel haptic capture system based on the local curvature of an object. We present a haptic sensor system comprising of three aligned and equally spaced fingers that move towards the surface of an object at the same speed. When an object is touched, our system records the relative times between each contact sensor. Simulating our approach in a virtual environment, we show that this new local and low-dimensional geometrical feature can be effectively used for shape recognition. Even with 10 samples, our system achieves an accuracy of over 90 % without using any sampling strategy or any associated spatial information.
KW - Haptic capture
KW - Haptic perception
KW - Robotic simulation
KW - Shape recognition
KW - Tactile recognition
UR - http://www.scopus.com/inward/record.url?scp=85118334810&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-89029-2_3
DO - 10.1007/978-3-030-89029-2_3
M3 - Conference contribution
AN - SCOPUS:85118334810
SN - 9783030890285
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 53
BT - Advances in Computer Graphics - 38th Computer Graphics International Conference, CGI 2021, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Magnenat-Thalmann, Nadia
A2 - Interrante, Victoria
A2 - Thalmann, Daniel
A2 - Papagiannakis, George
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 38th Computer Graphics International Conference, CGI 2021
Y2 - 6 September 2021 through 10 September 2021
ER -