Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

Understand

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.