Reinforcement learning-based method for grasping moving object with robotic manipulator

The paper proposes a method for controlling a robotic manipulator using reinforcement learning with deep neural networks to grasp moving objects on a conveyor belt. Unlike tasks involving static objects, this problem requires the consideration of dynamic factors, which significantly complicates the control process. The paper provides a detailed description of the physical and kinematic modeling of the manipulator, as well as the integration of manipulator and object parameters into the neural network structure. The method is tested in the PyBullet physics simulation environment.