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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Yuxiang Yang, Zhihao Ni, Mingyu Gao, Jing Zhang and Dacheng Tao, "Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 135-145, Jan. 2022. doi: 10.1109/JAS.2021.1004255
Citation: Yuxiang Yang, Zhihao Ni, Mingyu Gao, Jing Zhang and Dacheng Tao, "Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 135-145, Jan. 2022. doi: 10.1109/JAS.2021.1004255

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

doi: 10.1109/JAS.2021.1004255
Funds:  This work was supported by the National Natural Science Foundation of China (61873077, 61806062), Zhejiang Provincial Major Research and Development Project of China (2020C01110), and Zhejiang Provincial Key Laboratory of Equipment Electronics
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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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    Highlights

    • A novel collaborative pushing and grasping method is proposed for handling tightly stacked objects
    • An efficient non-maximum suppression policy is devised to suppress unreasonable actions
    • A novel PR-Net is devised to assess the degree of aggregation or separation between objects
    • A common household item dataset is established to train and evaluate the model

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