Скачать или смотреть Robotic Pick-And-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain I

ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod T.1
Authors: Zeng, Andy; Song, Shuran; Yu, Kuan-Ting; Donlon, Elliott; Hogan, Francois; Bauza Villalonga, Maria; Ma, Daolin; Taylor, Orion; Liu, Melody; Romo Grau, Eudald; Fazeli, Nima; Alet, Ferran; Chavan Dafle, Nikhil; Holladay, Rachel; Morona, Isabella; Nair, Prem; Green, Druck; Taylor, Ian; Liu, Weber; Funkhouser, Thomas A.; Rodriguez, Alberto
Title: Robotic Pick-And-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

Abstract:
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 http://arc.cs.princeton.edu