抓住
计算机科学
水准点(测量)
强化学习
人工智能
约束(计算机辅助设计)
对象(语法)
分离(统计)
机器人
国家(计算机科学)
机器学习
工程类
算法
机械工程
大地测量学
程序设计语言
地理
作者
Yuxiang Yang,Zhihao Ni,Mingyu Gao,Jing Zhang,Dacheng Tao
标识
DOI:10.1109/jas.2021.1004255
摘要
Directly grasping the tightly stacked objects may cause collisions and result in failures, degenerating the functionality of robotic arms. Inspired by the observation that first pushing objects to a state of mutual separation and then grasping them individually can effectively increase the success rate, we devise a novel deep Q-learning framework to achieve collaborative pushing and grasping. Specifically, an efficient non-maximum suppression policy (policyNMS) is proposed to dynamically evaluate pushing and grasping actions by enforcing a suppression constraint on unreasonable actions. Moreover, a novel data-driven pushing reward network called PR-Net is designed to effectively assess the degree of separation or aggregation between objects. To benchmark the proposed method, we establish a dataset containing common household items dataset (CHID) in both simulation and real scenarios. Although trained using simulation data only, experiment results validate that our method generalizes well to real scenarios and achieves a 97% grasp success rate at a fast speed for object separation in the real-world environment.
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