Evaluating PyBullet and Isaac Sim in the Scope of Robotics and Reinforcement Learning

Abstract

The choice of a physics simulator is a crucial decision in robotics and reinforcement learning (RL), significantly impacting the efficiency and effectiveness. This study conducts a comprehensive comparison of two popular simulator, PyBullet and Isaac sim, to identify their strengths and limitations in facilitating RL agent training. Each environment offers distinct features in terms of fidelity and computational efficiency, making it essential to evaluate their suitability for robotic reach task and their impact on RL algorithm performance. By benchmarking these environments, we aim to provide valuable insights for researchers and developers in the robotics community, informing them about the most suitable physics simulator for their specific application and ultimately advancing the state of the art in RL-based robotics research.