MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence
Cheryl Wang ⋅ Chun Kwang Tan ⋅ Balint Hodossy ⋅ Shirui Lyu ⋅ Jun Guo ⋅ Wentao Zhao ⋅ Huaping Liu ⋅ Chengkun Li ⋅ Merkourios Simos ⋅ Bianca Ziliotto ⋅ Alexander Mathis ⋅ Siyuan Liu ⋅ Jiahao Chen ⋅ Shanlin Zhong ⋅ Bo Jiang ⋅ Ci Song ⋅ Yaoye Zhu ⋅ Chenhui Zuo ⋅ Yanan Sui ⋅ Irfan Refai ⋅ Massimo Sartori ⋅ Guillaume Durandau ⋅ Vikash Kumar ⋅ Vittorio Caggiano
Abstract
Athletic performance represents the pinnacle of human motor intelligence, demanding rapid choices, precise control, agility, and coordinated physical execution. Replicating this seamless combination of capabilities remains elusive in current artificial intelligence and robotic systems. Concurrently, understanding the biological mastery of these movements is hindered because complex muscle coordination is rarely measured in vivo due to the limitations of physical equipment. To bridge this fundamental gap in understanding, $\textit{MyoChallenge}$ at NeurIPS 2025 established a pioneering benchmark for motor control intelligence in sports, leveraging high-fidelity musculoskeletal models within physics simulation combined with machine learning-driven algorithms. The competition introduces two distinct tracks emphasizing either upper or lower limbs control: a table tennis rally task utilizing a biomechanic upper limb composed of an arm with a hand (MyoArm) and a trunk (MyoTorso); and a soccer penalty kick using a biomechanic model of legs (MyoLeg) and a trunk (MyoTorso). Marking the fourth iteration of the MyoChallenge series, this event attracted almost 70 teams and over 560 submissions globally, uniting a diverse community ranging from physicians and neuroscientists to machine learning experts. The competition facilitated the development of several state-of-the-art control algorithms for a musculoskeletal system capable of sports agility, leveraging techniques such as physics-based motion planners, on-policy behaviour cloning, hierarchical planning, and muscle synergies that significantly surpassed our provided baseline performance. By integrating standardized tasks and physiologically realistic models into the open-source framework of MyoSuite, MyoChallenge'25 serves as a reproducible and reusable testbed to accelerate interdisciplinary research across machine learning, biomechanics, sports science, and neuroscience. Project page: https://www.myosuite.org//myochallenge/myochallenge-2025
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