High-fidelity Human Fingertip Models
The sensation of touch originates from the skin deformation, which in turn stimulates the mechanoreceptors that send neural signals to the brain. I am developing detailed computational models for investigating the deformation mechanics of the fingertip, which is one of the most sensitive areas of the human body. I aim to build simulation tools that will enable a deeper understanding of physical phenomena occuring within the soft tissue and skin-surface interface. Such numerical means can also be used for circumventing a portion of costly experimental procedures in the design of intelligent systems with the aim of delivering various realistic haptic experiences.
Calibrating Tactile Sensors with Simulated Data
Neural networks can also be trained with the simulated data! In this collaborative work, we use sim-to-real transfer learning for calibrating a soft tactile sensor electrical resistance tomography. with a Multiphysics Model and . We optimized the model parameters to reduce the gap between the simulation and reality. calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating soft ERT-based tactile sensors using sim-to-real transfer learning with a finite element multiphysics model. The model is composed of three simple models that together map contact pressure distributions to voltage measurements.
Simulation-driven Optimal Sensor Placement