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Learn to grasp unknown objects in robotic manipulation

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Abstract

Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects.

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Acknowledgements

This work was supported by NSERC Discovery Program under Grant RGPIN-2017-05762 and MITACS Accelerate under IT14727.

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Authors and Affiliations

  1. University of Ontario Institute of Technology, Oshawa, Canada

    Abdulrahman Al-Shanoon & Haoxiang Lang

  2. Kennesaw State University, Marietta, USA

    Ying Wang

  3. Senturing Technologies, Ltd., Vancouver, Canada

    Yunfei Zhang

  4. Xiamen University, Xiamen, China

    Wenxin Hong

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  1. Abdulrahman Al-Shanoon
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Correspondence to Haoxiang Lang.

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Al-Shanoon, A., Lang, H., Wang, Y. et al. Learn to grasp unknown objects in robotic manipulation. Intel Serv Robotics 14, 571–582 (2021). https://doi.org/10.1007/s11370-021-00380-9

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