Soft Finger Grasp Force and Contact State Estimation from Tactile Sensors

TL;DR

This paper explores estimating grasp forces and contact states using integrated tactile sensors on soft robotic fingers, aiming to enhance dexterity and grasp stability without object-specific training.

Contribution

It introduces a neural network-based method for force estimation from soft sensor data and validates its effectiveness in contact state detection during grasping tasks.

Findings

Force estimation accuracy demonstrated with sensor data

Contact state estimation validated in task scenarios

Neural network approach enables generalizable force sensing

Abstract

Soft robotic fingers can improve adaptability in grasping and manipulation, compensating for geometric variation in object or environmental contact, but today lack force capacity and fine dexterity. Integrated tactile sensors can provide grasp and task information which can improve dexterity,but should ideally not require object-specific training. The total force vector exerted by a finger provides general information to the internal grasp forces (e.g. for grasp stability) and, when summed over fingers, an estimate of the external force acting on the grasped object (e.g. for task-level control). In this study, we investigate the efficacy of estimating finger force from integrated soft sensors and use it to estimate contact states. We use a neural network for force regression, collecting labelled data with a force/torque sensor and a range of test objects. Subsequently, we apply this model in a plug-in task scenario and demonstrate its validity in estimating contact states.