TY - GEN
T1 - Poster Abstract
T2 - 23rd ACM Conference on Embedded Networked Sensor Systems, SenSys 2025
AU - Yazdnian, Vahid
AU - Shen, Ruiyi
AU - Ghasempour, Yasaman
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/5/6
Y1 - 2025/5/6
N2 - Sensing the coefficient of friction (COF) is crucial for robotic and Cyber-Physical System applications, including grasping. We introduce RoboTera, a novel system for non-contact COF estimation using sub-Terahertz (sub-THz) perception in robotics. Unlike tactile sensors that require direct contact, our approach leverages sub-THz signals with sub-millimeter wavelength to capture surface roughness characteristics as an essential factor in non-contact COF inference, that conventional imaging modalities like cameras and LiDAR cannot detect. Our system enables precise COF inference by integrating sub-THz-estimated roughness with image-based material classification. Further, we exploit COF inferences to identify stable grasp configurations and improve grasping performance. Experiments show over 92% accuracy in COF estimation, with a 31.8% improvement in grasp success rates in real-world robotic tasks.
AB - Sensing the coefficient of friction (COF) is crucial for robotic and Cyber-Physical System applications, including grasping. We introduce RoboTera, a novel system for non-contact COF estimation using sub-Terahertz (sub-THz) perception in robotics. Unlike tactile sensors that require direct contact, our approach leverages sub-THz signals with sub-millimeter wavelength to capture surface roughness characteristics as an essential factor in non-contact COF inference, that conventional imaging modalities like cameras and LiDAR cannot detect. Our system enables precise COF inference by integrating sub-THz-estimated roughness with image-based material classification. Further, we exploit COF inferences to identify stable grasp configurations and improve grasping performance. Experiments show over 92% accuracy in COF estimation, with a 31.8% improvement in grasp success rates in real-world robotic tasks.
KW - grasping
KW - robotic perception
KW - wireless terahertz sensing
UR - https://www.scopus.com/pages/publications/105035736340
UR - https://www.scopus.com/pages/publications/105035736340#tab=citedBy
U2 - 10.1145/3715014.3724038
DO - 10.1145/3715014.3724038
M3 - Conference contribution
AN - SCOPUS:105035736340
T3 - ACM SenSys 2025 - 23rd ACM Conference on Embedded Networked Sensor Systems, In Transactions to Conference Embedded Artificial Intelligence and Sensing Systems
SP - 622
EP - 623
BT - ACM SenSys 2025 - 23rd ACM Conference on Embedded Networked Sensor Systems, In Transactions to Conference Embedded Artificial Intelligence and Sensing Systems
PB - Association for Computing Machinery, Inc
Y2 - 6 May 2025 through 9 May 2025
ER -