視覚と触覚をつなぐクロスモーダル予測
Connecting Touch and Vision via Cross-Modal Prediction
視覚と触覚の相互変換を実現するため、ロボットに両センサーを搭載して大規模データセットを収集し、スケール差を考慮した条件付き敵対的モデルで触覚から視覚、視覚から触覚の生成を行う。
著者: Yunzhu Li, Jun-Yan Zhu, Russ Tedrake, Antonio Torralba
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. In this work, we investigate the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: while our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input. To accomplish our goals, we first equip robots with both visual and tactile sensors and collect a large-scale dataset of corresponding vision and tactile image sequences. To close the scale gap, we present a new conditional adversarial model that incorporates the scale and location information of the touch. Human perceptual studies demonstrate that our model can produce realistic visual images from tactile data and vice versa. Finally, we present both qualitative and quantitative experimental results regarding different system designs, as well as visualizing the learned representations of our model.
