触覚は把持成功を予測できるか：視覚と触覚のマルチモーダル学習による検証
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
GelSight触覚センサ付き2本指グリッパで9,000回以上の把持試行を収集し、視覚・触覚を組み合わせた深層学習モデルで把持成功を予測、触覚情報の追加が予測精度を大きく向上させることを示した。
著者: Roberto Calandra, Andrew Owens, Manu Upadhyaya, Wenzhen Yuan, Justin Lin, Edward H. Adelson, Sergey Levine
分類: cs.RO, cs.CV, cs.LG, stat.ML
原文アブストラクト
A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.
