Interactive Robotic Arm Simulation

Abstract

In the dynamic landscape of robotics and artificial intelligence, this research pioneers a groundbreaking fusion of simulation technology and advanced machine learning, specifically reinforcement learning, to enhance robotic arm capabilities. The focus centers on the utilization of a cutting-edge simulator, powered by the PyBullet physics engine, to faithfully replicate the intricate dynamics of a robotic arm within a digital environment. Serving as an experimental ground, the simulator enables the robotic arm to navigate, manipulate objects, and dynamically engage with its surroundings. Through a symbiotic relationship between simulation technology and reinforcement learning, this research focuses on an adaptive learning approach. This approach accelerates the robotic arm's skill acquisition, refining critical aspects such as precision, speed, and adaptability. The project contributes to the evolution of robotic arm capabilities, paving the way for more autonomous, versatile, and adept robotic systems in the realm of artificial intelligence and robotics.