Event-Driven Force Measurement of a Variable-Stiffness Robotic Finger Using Masked Autoencoder Pre-Training

Abstract

Force feedback in compliant robotic grippers is essential for precise manipulation tasks but remains challenging because irregular deformation of soft materials renders traditional sensor integration impractical. Event camera has emerged as a powerful alternative to conventional vision sensors through their ability to capture only temporal changes in a scene, thereby significantly reducing data redundancy and processing overhead. In this paper, we introduce a novel vision-based force prediction framework that employs a Mamba-Like architecture to process event camera data in compliant grippers. To validate our approach, we have developed a custom gripper with variable stiffness and created a comprehensive dataset comprising over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9,000</i> event frames. Our methodology combines self-supervised pre-training for learning rich feature representations with a Mamba-like regression framework to achieve accurate force prediction. The proposed method demonstrates a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.14</i> improvement in RMSE when compared to existing Vision Transformer approaches. Through extensive experimental validation-including real-time performance analysis, ablation studies, and generalization tests across various gripper configurations-we demonstrate the framework's effectiveness. Our results indicate robust performance suitable for practical industrial applications, suggesting potential extensions to other compliant robotics applications that require precise force estimation.