Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams
Zhi Su, Yuman Gao, Emily Lukas, Yunfei Li, Jiaze Cai, Faris Tulbah, Fei Gao, Chao Yu, Zhongyu Li, Yi Wu, Koushil Sreenath

TL;DR
This paper introduces a hierarchical multi-agent reinforcement learning framework enabling autonomous quadruped robot teams to perform complex cooperative and competitive soccer tasks, demonstrating real-world deployment and sophisticated team behaviors.
Contribution
It presents a novel decentralized MARL approach with hierarchical skills training and strategic planning for real-world quadruped robot soccer.
Findings
Agents achieve coordinated passing and interception behaviors
The system performs well in both indoor and outdoor environments
Decentralized policies enable autonomous real-world robot soccer matches
Abstract
Achieving coordinated teamwork among legged robots requires both fine-grained locomotion control and long-horizon strategic decision-making. Robot soccer offers a compelling testbed for this challenge, combining dynamic, competitive, and multi-agent interactions. In this work, we present a hierarchical multi-agent reinforcement learning (MARL) framework that enables fully autonomous and decentralized quadruped robot soccer. First, a set of highly dynamic low-level skills is trained for legged locomotion and ball manipulation, such as walking, dribbling, and kicking. On top of these, a high-level strategic planning policy is trained with Multi-Agent Proximal Policy Optimization (MAPPO) via Fictitious Self-Play (FSP). This learning framework allows agents to adapt to diverse opponent strategies and gives rise to sophisticated team behaviors, including coordinated passing, interception,…
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Taxonomy
TopicsTransportation and Mobility Innovations · Traffic control and management · Reinforcement Learning in Robotics
MethodsSparse Evolutionary Training
