A Deep Reinforcement Learning Enhanced Snow Geese Optimizer for Robot Calibration

Abstract

Accurate absolute positioning is essential for industrial robot arms, especially in high-precision manufacturing tasks. Traditional calibration methods often rely heavily on domain-specific knowledge and handcrafted algorithms, making it challenging for broader adoption across disciplines. To tackle this problem, this paper proposes a novel calibration framework based on an enhanced metaheuristic approach named RLSGA, which integrates deep reinforcement learning with the Snow Geese Algorithm (SGA). Unlike conventional strategies where the movement of agents is fully determined by predefined equations, the proposed method leverages a deep policy network to guide individual geese’s migration behavior. This network generates adaptive decisions regarding position updates, convergence direction, and flight mode selection. The learned policy enables more flexible and efficient exploration of the calibration parameter space. Experimental results on robot arm calibration tasks demonstrate that RLSGA achieves superior calibration accuracy and robustness compared to existing optimization-based methods, validating its effectiveness and potential for real-world applications.