MAKP: Multi-Mode Accurate Kicking Policy for Humanoid Robots
Zheng Zhang, Kaiyang Xu, Zhanxiang Cao, Yizhi Chen, Peng Wang, Haoyang Li, Yang Zhang, Shengcheng Fu
Abstract
Humanoid robot soccer players face fundamental challenges in achieving stable motion execution and ball trajectory control, particularly under balance constraints during single-leg support phases. In this paper, we introduce MAKP (Multi-mode Accurate Kicking Policy), a novel motion generation-based end-to-end kicking paradigm that enables humanoid robots to perform accurate ball kicking while executing diverse kicking motions. MAKP uniquely integrates a diffusion-based motion generator to produce varied kicking trajectories and employs a three-stage learning strategy to address the inherent trade-off between motion similarity and kicking performance. Stage I focuses on stable motion tracking and single-leg balance maintenance, while Stage II optimizes ball kicking capabilities. In Stage III, we introduce a Multi-Critic mechanism combined with curriculum learning to further enhance the balance between kicking accuracy, motion similarity and robot stability. Real-world experiments on the Booster T1 platform validate the effectiveness of our approach.