Efficient Sim2Real Training Using PPO and A2C for 6-DoF Robot Control

Robotic systems are widely used to enhance productivity, but traditional robotic arms rely on pre-planned movements, limiting adaptability in dynamic environments. This work investigates the use of reinforcement learning to train a 6-degree-of-freedom (6-DoF) robotic arm for grasp tasks without prior knowledge of the environment. We compare two algorithms-Advantage Actor-Critic (A2C) and Proxi-mal Policy Optimization (PPO)-within a simulation-to-reality (Sim-to-Real) framework. Our experimental results demonstrate that PPO outperforms A2C in both convergence speed and performance in simulated and real-world tasks, highlighting its effectiveness for sim-to-real applications and the importance of selecting the right algorithm for dynamic robotic control.