Sensing friction coefficient is vital for various cyber-physical system applications, including robotic grasping. We present RoboTera, a novel system for the non-contact coefficient of friction (COF) estimation using sub-Terahertz (sub-THz) perception in robotics for the first time. While advanced tactile sensors can provide friction inputs, they require direct contact, which might not be suitable for various applications. Non-contact estimation of friction between the gripper and a target object requires extracting the minute surface perturbations which is unfortunately not supported by existing imaging modalities (such as camera and LiDAR). Our key insight is that sub-THz signals are best suited to infer such information as their sub-millimeter wavelength is comparable with surface perturbations. Hence, impinging sub-THz waves on everyday objects creates diffuse backscattering whose spectral profile hints at surface texture properties. Leveraging this, we use sub-THz wireless signals to extract surface roughness. By integrating sub-THz-estimated roughness inputs with conventional image-based material classification schemes, RoboTera provides a non-contact and precise COF inference framework. Further, we exploit COF inferences to identify stable grasp configurations and improve grasping performance. Our experiments demonstrate an average accuracy of over 92% in COF estimation. We implemented RoboTera on a robotic arm to assess its real-world grasping performance, achieving a 31.8% average improvement across objects with diverse COF profiles and shapes.

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.

The extended near-field range in future mm-Wave and sub-THz wireless networks demands a precise and efficient near-field channel simulator for understanding and optimizing wireless communications in this less-explored regime. This paper presents NirvaWave, a novel near-field channel simulator, built on scalar diffraction theory and Fourier principles, that precisely captures the propagation evolution of arbitrary user-defined transmit electromagnetic (EM) signals in complex user-defined wireless mediums. NirvaWave offers an interface for investigating novel near-field wavefronts, e.g., Airy beams, Bessel beams, and the interaction of mmWave and sub-THz signals with obstructions, reflectors, and scatterers. The simulation run-time in NirvaWave is orders of magnitude lower than its EM software counterparts that directly solve the Maxwell equations. Hence, NirvaWave enables a user-friendly interface for large-scale channel simulations required for developing new model-driven and data-driven techniques in next-generation communication systems. We evaluated the performance of NirvaWave through direct comparison with EM simulation software. Finally, we have open-sourced the core code-base of NirvaWave in our GitHub repository.