Abstract This study addresses the critical demand for high-precision, low-noise gears in humanoid robot joint reducers. Conventional gear shaping processes typically achieve only GB/T 10,095.1-2022 Grade 6-7 precision, which cannot meet the requirements of silent transmission (30–45 dB). To solve this problem, an intelligent optimization framework based on Convolutional Deep Double Q-Network (CDDQN) is proposed for multi-error collaborative compensation in small-to-medium module gear shaping.
