This paper presents a robotic pick-and-place system that is capable of
grasping and recognizing both known and novel objects in cluttered
environments. The key new feature of the system is that it handles a wide range
of object categories without needing any task-specific training data for novel
objects. To achieve this, it first uses a category-agnostic affordance
prediction algorithm to select and execute among four different grasping
primitive behaviors. It then recognizes picked objects with a cross-domain
image classification framework that matches observed images to product images.
Since product images are readily available for a wide range of objects (e.g.,
from the web), the system works out-of-the-box for novel objects without
requiring any additional training data. Exhaustive experimental results
demonstrate that our multi-affordance grasping achieves high success rates for
a wide variety of objects in clutter, and our recognition algorithm achieves
high accuracy for both known and novel grasped objects. The approach was part
of the MIT-Princeton Team system that took 1st place in the stowing task at the
2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
available online at this http URL

本文介绍了一种具有抓取和识别能力的机器人抓取和放置系统，它可以在杂乱的环境中识别已知和新颖物体。该系统具有广泛的物品类别，无需为新物品准备任务特定的训练数据。实验结果表明，我们的多面抓取实现了高成功率，并且我们的识别算法实现了高准确率，是 Amazon Robotics Challenge 2017 中获得第一名的 MIT-Princeton Team 系统的一部分。