マルチタスク学習と位置損失による単眼画像からのロボット把持性能の改善
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss
単眼カラー画像からの把持推定CNNに、深度再構成の補助タスクと把持可能点のみに着目する位置損失を導入し、把持成功率を72%から79%へ向上させた研究。
著者: William Prew, Toby Breckon, Magnus Bordewich, Ulrik Beierholm
分類: cs.RO, cs.CV
原文アブストラクト
In this paper, we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our multi-task CNN model improves grasping performance from a baseline average of 72.04% to 78.14% on the large Jacquard grasping dataset when performing a supplementary depth reconstruction task. The second is introducing a positional loss function that emphasises loss per pixel for secondary parameters (gripper angle and width) only on points of an object where a successful grasp can take place. This increases performance from a baseline average of 72.04% to 78.92% as well as reducing the number of training epochs required. These methods can be also performed in tandem resulting in a further performance increase to 79.12% while maintaining sufficient inference speed to afford real-time grasp processing.
