触覚テクスチャ生成のための動作条件付きモデルの学習
Learning an Action-Conditional Model for Haptic Texture Generation
視覚触覚センサとユーザ動作を入力に、素材ごとに別モデルを必要とせず全素材を統合した動作条件付きモデルで触覚振動フィードバック用の加速度を予測する手法を提案した。
著者: Negin Heravi, Wenzhen Yuan, Allison M. Okamura, Jeannette Bohg
分類: cs.RO, cs.LG
原文アブストラクト
Rich haptic sensory feedback in response to user interactions is desirable for an effective, immersive virtual reality or teleoperation system. However, this feedback depends on material properties and user interactions in a complex, non-linear manner. Therefore, it is challenging to model the mapping from material and user interactions to haptic feedback in a way that generalizes over many variations of the user's input. Current methodologies are typically conditioned on user interactions, but require a separate model for each material. In this paper, we present a learned action-conditional model that uses data from a vision-based tactile sensor (GelSight) and user's action as input. This model predicts an induced acceleration that could be used to provide haptic vibration feedback to a user. We trained our proposed model on a publicly available dataset (Penn Haptic Texture Toolkit) that we augmented with GelSight measurements of the different materials. We show that a unified model over all materials outperforms previous methods and generalizes to new actions and new instances of the material categories in the dataset.
