Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning

Yuxiang Yang; Zhihao Ni; Mingyu Gao; Jing Zhang; Dacheng Tao

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Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning

机译：Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning

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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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《自动化学报（英文版）》 |2022年第1期|135-145|共11页
作者
Yuxiang Yang; Zhihao Ni; Mingyu Gao; Jing Zhang; Dacheng Tao;
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作者单位

School of Electronics and Information Hangzhou Dianzi University Hangzhou;

Zhejiang Provincial Key Laboratory of Equipment Electronics Hangzhou 310018 China;

School of Electronics and Information Hangzhou Dianzi University Hangzhou;

Zhejiang Provincial Key Laboratory of Equipment Electronics Hangzhou 310018 China;

School of Electronics and Information Hangzhou Dianzi University Hangzhou;

Zhejiang Provincial Key Laboratory of Equipment Electronics Hangzhou 310018 China;

School of Computer Science Faculty of Engineering University of Sydney Darlington NSW 2006 Australia;

JD Explore Academy JD.com Beijing 101111 China;

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Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning

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