標題: Pose-Aware Placement of Objects with Semantic Labels - Brandname-based Affordance Prediction and Cooperative Dual-Arm Active Manipulation
作者: Su, Yung-Shan
Lu, Shao-Huang
Ser, Po-Sheng
Hsu, Wei-Ting
Lai, Wei-Cheng
Xie, Biao
Huang, Hong-Ming
Lee, Teng-Yok
Chen, Hung-Wen
Yu, Lap-Fai
Wang, Hsueh-Cheng
電機工程學系
Department of Electrical and Computer Engineering
公開日期: 1-一月-2019
摘要: The Amazon Picking Challenge and the Amazon Robotics Challenge have shown significant progress in object picking from a cluttered scene, yet object placement remains challenging. It is useful to have pose-aware placement based on human and machine readable pieces on an object. For example, the brandname of an object placed on a shelf should be facing the human customers. The robotic vision challenges in the object placement task: a) the semantics and geometry of the object to be placed need to be analysed jointly; b) and the occlusions among objects in a cluttered scene could make it hard for proper understanding and manipulation. To overcome these challenges, we develop a pose-aware placement approach by spotting the semantic labels (e.g., brandnames) of objects in a cluttered tote and then carrying out a sequence of actions to place the objects on a shelf or on a conveyor with desired poses. Our major contributions include 1) providing an open benchmark dataset of objects and brandnames with multi-view segmentation for training and evaluations; 2) carrying out comprehensive evaluations for our brandname-based fully convolutional network (FCN) that can predict the affordance and grasp to achieve pose-aware placement, whose success rates decrease along with clutters; 3) showing that active manipulation with two cooperative manipulators and grippers can effectively handle the occlusion of brandnames. We analyzed the success rates and discussed the failure cases to provide insights for future applications.
URI: http://hdl.handle.net/11536/155264
ISBN: 978-1-7281-4004-9
ISSN: 2153-0858
期刊: 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
起始頁: 4760
結束頁: 4767
顯示於類別:會議論文