Performance Comparison of Typical Physics Engines Using Robot Models With Multiple Joints

Chengye Liao, Yarong Wang, Xuda Ding, Yi Ren, Xiaoming Duan, Jianping He

Abstract: Physics engines are essential components in simulating complex robotic systems. The accuracy and computational speed of these engines are crucial for reliable real-time simulation. This letter comprehensively evaluates the performance of five common physics engines, i.e., ODE, Bullet, DART, MuJoCo, and PhysX, and provides guidance on their suitability for different scenarios. Specifically, we conduct three experiments using complex multi-joint robot models to test the stability, accuracy, and friction effectiveness. Instead of using simple implicit shapes, we use complete robot models that better reflect real-world scenarios. In addition, we conduct experiments under the default most suitable simulation environment configuration for each physics engine. Our results show that MujoCo performs best in linear stability, PhysX in angular stability, MuJoCo in accuracy, and DART in friction simulations.