Integrating Predictive Sampling and Diffusion Policy for Adaptive Control in Simulated Robotic Arms

Abstract

This paper presents a hybrid control approach that combines Predictive Sampling and Diffusion Policy to achieve smooth and adaptive motion in robotic arms. Traditional control systems such as PID and Model Predictive Control provide stability but have limited adaptability under uncertain or changing conditions. In contrast, diffusion models can generate smooth control actions but do not ensure physical consistency. In the proposed method, Predictive Sampling is used to plan short term control actions based on the robot dynamics, while the Diffusion Policy refines these actions through a learning based denoising process. The system is implemented on a 2R robotic arm using the MuJoCo simulator with Python and PyTorch. Experimental results show that the hybrid controller produces smoother trajectories, reduces torque noise, and improves overall motion stability when compared to conventional control methods. The results demonstrate how combining model based and data driven techniques can enhance adaptive control in robotic systems.