HyperTaxel: 対照学習による触覚センサ信号のハイパー解像度化
HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning
疎な低解像度の触覚センサ信号を対照学習で高解像度の接触面にマッピングし、接触面の形状特徴を捉える表現を学習するフレームワークを提案した。
著者: Hongyu Li, Snehal Dikhale, Jinda Cui, Soshi Iba, Nawid Jamali
分類: cs.RO, cs.AI
原文アブストラクト
To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-standardized representations. In this paper, we propose a novel framework, HyperTaxel, for learning a geometrically-informed representation of taxel-based tactile signals to address challenges associated with their spatial resolution. We use this representation and a contrastive learning objective to encode and map sparse low-resolution taxel signals to high-resolution contact surfaces. To address the uncertainty inherent in these signals, we leverage joint probability distributions across multiple simultaneous contacts to improve taxel hyper-resolution. We evaluate our representation by comparing it with two baselines and present results that suggest our representation outperforms the baselines. Furthermore, we present qualitative results that demonstrate the learned representation captures the geometric features of the contact surface, such as flatness, curvature, and edges, and generalizes across different objects and sensor configurations. Moreover, we present results that suggest our representation improves the performance of various downstream tasks, such as surface classification, 6D in-hand pose estimation, and sim-to-real transfer.
