功能可见性
计算机科学
人工智能
计算机视觉
对象(语法)
领域(数学分析)
特征(语言学)
杂乱
机器人学
任务(项目管理)
匹配(统计)
钥匙(锁)
多样性(控制论)
机器人
人机交互
工程类
数学
数学分析
哲学
统计
系统工程
雷达
电信
语言学
计算机安全
作者
Andy Zeng,Shuran Song,Kuan‐Ting Yu,Elliott Donlon,Francois R. Hogan,Maria Bauzá,Dongli Ma,Orion Taylor,Melody Liu,Eudald Romo,Nima Fazeli,Ferran Alet,Nikhil Chavan Dafle,Rachel Holladay,Isabella Morona,Prem Qu Nair,Druck Green,Ian Taylor,Weber Liu,Thomas Funkhouser,Alberto Rodríguez
标识
DOI:10.1177/0278364919868017
摘要
This article 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 an object-agnostic grasping framework to map from visual observations to actions: inferring dense pixel-wise probability maps of the affordances for four different grasping primitive actions. It then executes the action with the highest affordance and 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 data collection or re-training. 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 first 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/
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