実時間触覚テクスチャレンダリングのための学習ベースモデルの開発と評価
Development and Evaluation of a Learning-based Model for Real-time Haptic Texture Rendering
視覚ベース触覚センサGelSightのデータを用い、ユーザの動作に条件付けられた深層学習モデルで様々な素材の触覚テクスチャ振動をリアルタイムに生成し、その知覚品質をユーザ研究で評価した。
著者: Negin Heravi, Heather Culbertson, Allison M. Okamura, Jeannette Bohg
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
Current Virtual Reality (VR) environments lack the rich haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The result of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need for learning a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.
