気圧式触覚センサと時間畳み込みニューラルネットワークによる滑り検出の学習
Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network
気圧式触覚センサのデータから時間畳み込みニューラルネットワークで物体の滑りを検出する手法を提案し、実世界の把持タスクで汎化性能を示した。
著者: Abhinav Grover, Philippe Nadeau, Christopher Grebe, Jonathan Kelly
分類: cs.RO, cs.LG
原文アブストラクト
The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from inexpensive, off-the-shelf components. We train a temporal convolution neural network to detect slip, achieving high detection accuracies while displaying robustness to the speed and direction of the slip motion. Further, we test our detector on two manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.
