Dense Force Estimation with an Event-based Optical Tactile Sensor
2026-06-08 • Robotics
RoboticsComputer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors developed a new method that uses event-based optical tactile sensors to measure how forces are applied across a surface in 3D, not just the total force. They track tiny surface movements using a mix of a special marker tracking technique and a neural network, then convert these movements into force estimates using a physics-based model. Their method works quickly and accurately, offering detailed touch feedback which could help robots grasp and handle objects more skillfully. This is the first time such dense 3D force data has been obtained from these fast sensors.
event-based sensoroptical tactile sensor3D force reconstructioninverse Finite Elements Methodsurface displacementshear displacementconvolutional neural networkdexterous manipulationforce feedbackrobotic grasping
Authors
Agis Politis, René Zurbrügg, Valentina Cavinato
Abstract
Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting only net forces. We introduce the first framework for dense 3D force field reconstruction using event-based optical tactile sensors. Our approach estimates 3D surface displacements from event data and maps them to forces via the inverse Finite Elements Method (iFEM). Shear displacements are recovered through the proposed event-based marker tracking algorithm, while normal displacements are predicted by a convolutional neural network trained on a collected dataset of synchronized force-displacement-event data. Experiments demonstrate accurate reconstruction of physically grounded forces, achieving a mean absolute error of (0.14 N, 0.10 N, 0.93 N) over force ranges up to (4 N, 4 N, 20 N), while operating at an average of 100 Hz. This work constitutes a first step toward enabling dense force feedback for high-frequency control in robotic grasping and dexterous manipulation.