Policy Stability Associated with Reinforcement Rewards in Robotic Manipulation Tasks

Abstract

The deployment of reinforcement learning in autonomous robotic manipulation presents profound challenges, particularly concerning policy stability when transitioning from simulated environments to physical field settings. This paper provides a comprehensive academic investigation into how various reinforcement reward structures impact the operational stability of control policies in real world robotic manipulation tasks. By conducting extensive field experiments utilizing a six degree of freedom robotic manipulator, this study bridges the simulation to reality gap that often confounds theoretical reinforcement learning models. The research isolates three primary reward architectures, specifically sparse rewards, manually engineered dense rewards, and intrinsically motivated hybrid rewards, evaluating their respective influences on policy convergence, robustness to environmental perturbations, and long term execution stability. Findings indicate that while dense reward formulations accelerate initial policy acquisition, they frequently induce brittle behavioral paradigms that degrade rapidly under field conditions characterized by sensory noise and unmodeled physical dynamics. Conversely, policies trained via intrinsically motivated hybrid reward structures demonstrate a statistically significant enhancement in stability and perturbation recovery. This paper systematically unpacks the methodological design, the empirical results derived from the physical trials, and the broader implications for the development of resilient autonomous systems. By foregrounding field experiment evidence, the study contributes critical insights into the necessity of physically grounded reward shaping for advancing the reliability of machine learning applications in industrial and dynamic environments.