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Novel objects 3-D dense packing through robotic pushing

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Abstract

Robotic picking and placing systems can increase the flexibility of packing objects in a box and are attractive in many fields. However, owing to inevitable uncertainties in both the picking and placing stages, the objects cannot be placed at desired positions accurately and hence cannot be packed densely. This paper presents an additional pushing action that maximizes the packing density; i.e., after being released from the robot end, the object is moved by robotic pushing actions to arrange the packing densely. The robotic pushing strategy is determined through a deep reinforcement learning algorithm. The idea is to compress the objects toward a corner to improve the volume utilization rate by minimizing the result of a heuristic score. The learning process is implemented in simulation and the trained network is transferred to a robot system directly. Simulations and experiments are presented for the packing of regular and irregular objects to verify the proposed method.

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Authors and Affiliations

  1. Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    JianHua Wu, HaoDong Zhang, ZhenHua Xiong & XiangYang Zhu

  2. Beijing Institute of Control Engineering, Beijing, 100190, China

    JianHua Wu & YaFei Chang

Authors
  1. JianHua Wu
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  2. HaoDong Zhang
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  3. YaFei Chang
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  4. ZhenHua Xiong
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  5. XiangYang Zhu
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Corresponding author

Correspondence to JianHua Wu.

Additional information

This work was supported by the Science and Technical Innovation 2030-Artificial Intelligence of New Generation (Grant No. 2018AAA0102704), the National Natural Science Foundation of China (Grant No. U1813224), and the State Key Laboratory of Mechanical System and Vibration (Grant No. MSVZD202205)

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Wu, J., Zhang, H., Chang, Y. et al. Novel objects 3-D dense packing through robotic pushing. Sci. China Technol. Sci. 65, 2942–2951 (2022). https://doi.org/10.1007/s11431-022-2182-y

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  • DOI: https://doi.org/10.1007/s11431-022-2182-y

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