触覚再把持：触覚変換シミュレーションによる把持調整
Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations
触覚計測を仮想的に変換して把持品質を評価するネットワークを用い、局所的な把持調整動作を計画する手法を提案した。把持成功率が平均70%向上した。
著者: Francois R. Hogan, Maria Bauza, Oleguer Canal, Elliott Donlon, Alberto Rodriguez
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments. Our approach determines regrasp actions by virtually searching for local transformations of tactile measurements that improve the quality of the grasp. First, we construct a tactile-based grasp quality metric using a deep convolutional neural network trained on over 2800 grasps. The quality of each grasp, a continuous value between 0 and 1, is determined experimentally by measuring its resistance to external perturbations. Second, we simulate the tactile imprints associated with robot motions relative to the initial grasp by performing rigid-body transformations of the given tactile measurements. The newly generated tactile imprints are evaluated with the learned grasp quality network and the regrasp action is chosen to maximize the grasp quality. Results show that the grasp quality network can predict the outcome of grasps with an average accuracy of 85% on known objects and 75% on a cross validation set of 12 objects. The regrasp control policy improves the success rate of grasp actions by an average relative increase of 70% on a test set of 8 objects.
