论文标题
杂乱的食物用自适应手指和合成数据训练的对象检测
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection
论文作者
论文摘要
食品包装行业即使在一种食物中，也可以处理各种各样的食品。菜单也是多种多样的，并且经常变化，从而使选择和位置的自动化变得困难。采摘bin的一种流行方法是使用实例分割方法首先识别托盘中的每片食物。但是，训练这些方法的人类注释不可靠且容易出错，因为食物与不明确的边界和视觉相似性相连，从而使碎片的分离变得困难。为了解决这个问题，我们提出了一种纯粹在合成数据上训练的方法，并通过使用SIM2Real方法成功地将填充食品托盘的数据集用于培训实例分割模型，从而成功地转移到现实世界中。另一个问题是，食物在抓握期间很容易受损。我们通过引入另外两种方法来解决这一问题：一种新型的自适应手指机制在发生碰撞时被动缩回，以及一种过滤抓取的方法，该方法可能会在掌握期间对邻近食物造成损害。我们证明了所提出的方法对几种真实食品的有效性。
The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-place difficult. A popular approach to bin-picking is to first identify each piece of food in the tray by using an instance segmentation method. However, human annotations to train these methods are unreliable and error-prone since foods are packed close together with unclear boundaries and visual similarity making separation of pieces difficult. To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models. Another concern is that foods are easily damaged during grasping. We address this by introducing two additional methods -- a novel adaptive finger mechanism to passively retract when a collision occurs, and a method to filter grasps that are likely to cause damage to neighbouring pieces of food during a grasp. We demonstrate the effectiveness of the proposed method on several kinds of real foods.
