[Paper] RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning

Source: arXiv - 2606.11092v1

Overview

Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven reinforcement learning (RL) provides stability in whole-body movement coordination, but a fixed reference makes it hard to adapt to varied ball positions and strike timings; in contrast, task reward-driven RL struggles to explore and discover valid kicks from scratch. We therefore introduce RoboNaldo, a three-stage motion-guided curriculum RL framework for high-impulse humanoid interaction. A single human-kick reference is used as a scaffold and progressively shifts optimization towards shooting performance. The curriculum first learns a stable whole-body kicking prior, then adapts the kick to free-kick settings where the ball is stationary at random positions, and finally extends it to moving-ball shooting through a locomotion-command and kick-trigger interface. A high-level heuristic planner controls this interface during training, while alternative high-level controllers can drive the same low-level policy at inference. In simulation, RoboNaldo demonstrates free-kick shot error 48.6% lower and shoot velocity 2.96x than prior work baselines. In real world on a Unitree G1 with onboard perception, RoboNaldo attains 0.73 m and 0.86 m average target shooting error from 3 m away in free-kick and moving-ball cases, accordingly. And the post-contact ball velocity reaches 13.10 m/s, which is 59-71% of reported professional open-play shot speed. Project page: $\href{https://opendrivelab.com/RoboNaldo}{\text{opendrivelab.com/RoboNaldo}}$.

Key Contributions

This paper presents research in the following areas:

• cs.RO
• cs.AI

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.RO.

Authors

• Yichao Zhong
• Yidan Lu
• Yuhang Lu
• Tianyang Tang
• Haoguang Mai
• Yixuan Pan
• Tianyu Li
• Li Chen
• Jingbo Wang
• Zhongyu Li
• Peng Lu
• Hongyang Li

Paper Information

• arXiv ID: 2606.11092v1
• Categories: cs.RO, cs.AI
• Published: June 9, 2026
• PDF: Download PDF