{"ID":2832324,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06571","arxiv_id":"2512.06571","title":"Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input","abstract":"Learning fast and robust ball-kicking skills is a critical capability for humanoid soccer robots, yet it remains a challenging problem due to the need for rapid leg swings, postural stability on a single support foot, and robustness under noisy sensory input and external perturbations (e.g., opponents). This paper presents a reinforcement learning (RL)-based system that enables humanoid robots to execute robust continual ball-kicking with adaptability to different ball-goal configurations. The system extends a typical teacher-student training framework -- in which a \"teacher\" policy is trained with ground truth state information and the \"student\" learns to mimic it with noisy, imperfect sensing -- by including four training stages: (1) long-distance ball chasing (teacher); (2) directional kicking (teacher); (3) teacher policy distillation (student); and (4) student adaptation and refinement (student). Key design elements -- including tailored reward functions, realistic noise modeling, and online constrained RL for adaptation and refinement -- are critical for closing the sim-to-real gap and sustaining performance under perceptual uncertainty. Extensive evaluations in both simulation and on a real robot demonstrate strong kicking accuracy and goal-scoring success across diverse ball-goal configurations. Ablation studies further highlight the necessity of the constrained RL, noise modeling, and the adaptation stage. This work presents a system for learning robust continual humanoid ball-kicking under imperfect perception, establishing a benchmark task for visuomotor skill learning in humanoid whole-body control.","short_abstract":"Learning fast and robust ball-kicking skills is a critical capability for humanoid soccer robots, yet it remains a challenging problem due to the need for rapid leg swings, postural stability on a single support foot, and robustness under noisy sensory input and external perturbations (e.g., opponents). This paper pres...","url_abs":"https://arxiv.org/abs/2512.06571","url_pdf":"https://arxiv.org/pdf/2512.06571v3","authors":"[\"Zifan Xu\",\"Myoungkyu Seo\",\"Dongmyeong Lee\",\"Hao Fu\",\"Jiaheng Hu\",\"Jiaxun Cui\",\"Yuqian Jiang\",\"Zhihan Wang\",\"Anastasiia Brund\",\"Joydeep Biswas\",\"Peter Stone\"]","published":"2025-12-06T21:27:50Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}