Vision-based grasping method for 7-DOF manipulator using reinforcement learning

The article describes the solution to the inverse kinematics problem for object grasping with the 7-DOF Franka Emika Panda manipulator, implemented with computer vision. In proposed solution, the robotic arm operates in a physical simulation environment, utilizing reinforcement learning algorithms from machine learning, supplemented by computer vision algorithms for geometric structure-based target localization and training, enabling the implementation of the entire algorithmic process. The process of problem solving, constructing the corresponding environment, and analyzing the outcomes of the integrated algorithm, which incorporates neural networks, demonstrates its capability to effectively solve complex inverse kinematics tasks. This cost-effective modern technique applies widely to similar tasks with other robotic manipulators.