Effective Estimation of Contact Force and Torque for Vision-based Tactile Sensor with Helmholtz-Hodge Decomposition
This work addresses the challenge of retrieving rich contact information for robotic tactile sensing, which is crucial for effective perception in robotics, though it appears incremental as it builds on existing methods for specific sensor types.
The paper tackled the problem of estimating contact force and torque from vision-based tactile sensors by proposing a method using Helmholtz-Hodge Decomposition on deformation vector fields, achieving verification through experiments with prediction error and variance metrics.
Retrieving rich contact information from robotic tactile sensing has been a challenging, yet significant task for the effective perception of object properties that the robot interacts with. This work is dedicated to developing an algorithm to estimate contact force and torque for vision-based tactile sensors. We first introduce the observation of the contact deformation patterns of hyperelastic materials under ideal single-axial loads in simulation. Then based on the observation, we propose a method of estimating surface forces and torque from the contact deformation vector field with the Helmholtz-Hodge Decomposition (HHD) algorithm. Extensive experiments of calibration and baseline comparison are followed to verify the effectiveness of the proposed method in terms of prediction error and variance. The proposed algorithm is further integrated into a contact force visualization module as well as a closed-loop adaptive grasp force control framework and is shown to be useful in both visualization of contact stability and minimum force grasping task.