Search code, repositories, users, issues, pull requests...
This project provides a Deep Reinforcement Learning (DRL) environment built with PyBullet and Gymnasium, simulating a UR5 robotic arm performing a peg-in-hole insertion task—a classic challenge in precision robotic assembly.
The UR5 robot is controlled via its end-effector axis, guiding a peg into a hole on a fixed object. A camera is mounted directly on the robot’s end-effector, capturing 100×100 grayscale images as visual input. This configuration enables eye-in-hand visual servoing, where the robot perceives and reacts to its environment from its own point of view.
In testing, the SAC agent was evaluated over 1000 episodes, achieving a final success rate of 95.6% in completing the peg-in-hole task. This demonstrates the algorithm's strong performance in precision control and robust policy learning under visual feedback.
