触覚センサによるソフトフィンガーの把持力と接触状態の推定
Soft Finger Grasp Force and Contact State Estimation from Tactile Sensors
ソフトロボットフィンガーに統合した触覚センサから、ニューラルネットワークで指の力を推定し、物体に依存しない接触状態推定を実現した研究。
著者: Hun Jang, Joonbum Bae, Kevin Haninger
分類: cs.RO
原文アブストラクト
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.
