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Novel objects 3-D dense packing through robotic pushing

Science China(Technological Sciences) 2022年第12期65卷 2942-2951页

作者： WU JianHua ZHANG HaoDong CHANG YaFei XIONG ZhenHua ZHU XiangYang Institute of robotics School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Beijing Institute of Control Engineering Beijing 100190China

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.

关键词： robotic pushing three-dimensional packing reinforcement learning

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

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 135-145页

作者： Yuxiang Yang Zhihao Ni Mingyu Gao Jing Zhang Dacheng Tao School of Electronics and Information Hangzhou Dianzi UniversityHangzhouand also with Zhejiang Provincial Key Laboratory of Equipment ElectronicsHangzhou 310018China School of Computer Science Faculty of EngineeringUniversity of SydneyDarlingtonNSW 2006Australia JD Explore Academy ***Beijing 101111China

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.

关键词： Convolutional neural network deep Q-learning(DQN) reward function robotic grasping robotic pushing

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