分布接触力計測による滑り検出：データ駆動型触覚センサを活用した物理ベース手法
Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor
視覚ベース触覚センサで分布力を計測し、物理モデルに基づいて把持中の物体の滑りをリアルタイム検出して把持力増加を促す手法を提案した。
著者: Pietro Griffa, Carmelo Sferrazza, Raffaello D'Andrea
分類: cs.RO, cs.CV
原文アブストラクト
Grasping objects whose physical properties are unknown is still a great challenge in robotics. Most solutions rely entirely on visual data to plan the best grasping strategy. However, to match human abilities and be able to reliably pick and hold unknown objects, the integration of an artificial sense of touch in robotic systems is pivotal. This paper describes a novel model-based slip detection pipeline that can predict possibly failing grasps in real-time and signal a necessary increase in grip force. As such, the slip detector does not rely on manually collected data, but exploits physics to generalize across different tasks. To evaluate the approach, a state-of-the-art vision-based tactile sensor that accurately estimates distributed forces was integrated into a grasping setup composed of a six degrees-of-freedom cobot and a two-finger gripper. Results show that the system can reliably predict slip while manipulating objects of different shapes, materials, and weights. The sensor can detect both translational and rotational slip in various scenarios, making it suitable to improve the stability of a grasp.
