圧力センサで滑りを検知する学習手法
Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors
気圧式触覚センサのデータから学習により滑りを検出し、91%以上の精度を達成、実環境の物体操作タスクでも汎化を実証した論文。
著者: Abhinav Grover, Christopher Grebe, Philippe Nadeau, Jonathan Kelly
分類: cs.RO, cs.AI
原文アブストラクト
Despite the utility of tactile information, tactile sensors have yet to be widely deployed in industrial robotics settings. Part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. Although these sensors have a low resolution, they have many other desirable properties including high reliability and durability, a very slim profile, and a low cost. We are able to achieve slip detection accuracies of greater than 91% while being robust to the speed and direction of the slip motion. Further, we test our detector on two robot manipulation tasks involving common household objects and demonstrate successful generalization to real-world scenarios not seen during training. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for complex manipulation tasks such as slip compensation.
