#7572. Learn to grasp unknown objects in robotic manipulation

October 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computational Mechanics;
Engineering (miscellaneous);
Mechanical Engineering;
Artificial Intelligence;
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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.
Keywords:
Autonomous system; Convolutional neural network; Deep reinforcement learning; Robotic manipulation; Visual servoing

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