物理ベースとデータ駆動の枠組みによる触覚信号の解釈と予測
Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework
BioTac触覚センサの大規模データセットと3D有限要素モデルを構築し、生信号から高密度変形場をニューラルネットで予測する手法を提案した。
著者: Yashraj S. Narang, Karl Van Wyk, Arsalan Mousavian, Dieter Fox
分類: cs.RO
原文アブストラクト
High-density afferents in the human hand have long been regarded as essential for human grasping and manipulation abilities. In contrast, robotic tactile sensors are typically used to provide low-density contact data, such as center-of-pressure and resultant force. Although useful, this data does not exploit the rich information content that some tactile sensors (e.g., the SynTouch BioTac) naturally provide. This research extends robotic tactile sensing beyond reduced-order models through 1) the automated creation of a precise tactile dataset for the BioTac over diverse physical interactions, 2) a 3D finite element (FE) model of the BioTac, which complements the experimental dataset with high-resolution, distributed contact data, and 3) neural-network-based mappings from raw BioTac signals to low-dimensional experimental data, and more importantly, high-density FE deformation fields. These data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible.
