A Hierarchical, Model-Based System for High-Performance Humanoid Soccer
Quanyou Wang, Mingzhang Zhu, Ruochen Hou, Kay Gillespie, Alvin Zhu, Shiqi Wang, Yicheng Wang, Gaberiel I. Fernandez, Yeting Liu, Colin Togashi, Hyunwoo Nam, Aditya Navghare, Alex Xu, Taoyuanmin Zhu, Min Sung Ahn, Arturo Flores Alvarez, Justin Quan, Ethan Hong, Dennis W. Hong

TL;DR
This paper details a comprehensive hierarchical system combining hardware innovations and integrated software for high-performance humanoid soccer robots, exemplified by ARTEMIS's victory at RoboCup 2024.
Contribution
It introduces a new humanoid platform with advanced actuators and a unified perception, navigation, and decision-making software architecture for competitive soccer playing.
Findings
Achieved robust, high-speed gameplay in RoboCup 2024
Developed a reliable perception and localization system
Demonstrated effective coordination and decision-making in dynamic matches
Abstract
The development of athletic humanoid robots has gained significant attention as advances in actuation, sensing, and control enable increasingly dynamic, real-world capabilities. RoboCup, an international competition of fully autonomous humanoid robots, provides a uniquely challenging benchmark for such systems, culminating in the long-term goal of competing against human soccer players by 2050. This paper presents the hardware and software innovations underlying our team's victory in the RoboCup 2024 Adult-Sized Humanoid Soccer Competition. On the hardware side, we introduce an adult-sized humanoid platform built with lightweight structural components, high-torque quasi-direct-drive actuators, and a specialized foot design that enables powerful in-gait kicks while preserving locomotion robustness. On the software side, we develop an integrated perception and localization framework that…
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Taxonomy
TopicsRobotic Locomotion and Control · Social Robot Interaction and HRI · Robot Manipulation and Learning
