This paper explores the application of Deep Reinforcement Learning (DRL) in human-robot collaborative sorting tasks, emphasizing the robot’s adaptability. DRL enables robots to adjust to dynamic environments, including human presence and unknown objects. Using RGB and depth images processed by DenseNet, the robot recognizes and manipulates objects. The proposed approach estimates grasping positions without prior object knowledge, enhancing adaptability in unstructured environments. A Deep Q-Network (DQN) simulation in CoppeliaSim evaluates execution speed, accuracy, and system robustness, demonstrating improved sorting efficiency and safety in human-robot collaboration.


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    Title :

    Deep Reinforcement Learning for Adaptive Object Sorting in Collaborative Robotics


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    International Conference “New Technologies, Development and Applications” ; 2025 ; Sarajevo, Bosnia and Herzegovina June 26, 2025 - June 28, 2025



    Publication date :

    2025-06-27


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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