Integrating Deep Learning for Object Manipulation: A 7-DOF Robotic Arm Perspective on Grasping

The Robotic arm with 7-Degree of Freedom (DOF) is extensively used in numerous industrial applications. However, its precision and control need further improvement for optimum results in various generalized applications. This paper presents a novel approach to improve the manipulation capabilities of a 7-DOF robotic arm by integrating the YOLOv7 object detection model and a Deep Reinforcement Learning (DRL) framework for control. YOLOv7 is employed to provide real-time perception, enabling accurate object recognition, while the DRL algorithm optimizes control by adapting to the dynamic environment of the robotic arm. The DRL algorithm learns through trial and error, adapting to the specific dynamics of the robotic arm and its environment. As a result, improved precision, stability, and adaptability were observed across various tasks. The primary contribution of this work is the optimization and integration of YOLOv7 with a Raspberry Pi, facilitating efficient and real-time object manipulation even on resource-constrained hardware. The proposed algorithm was trained on diverse datasets, enabling the system to generalize effectively across multiple objects and real-world scenarios. E