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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.15650 |
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| _version_ | 1866913131986419712 |
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| author | Wang, Cheryl Tan, Chun Kwang Hodossy, Balint K. Lyu, Eric Guo, Jun Zhao, Wentao Liu, Huaping Li, Chengkun Simos, Merkourios Ziliotto, Bianca Mathis, Alexander Liu, Siyuan Chen, Jiahao Zhong, Shanlin Jiang, Bo Song, Ci Zhu, Yaoye Zuo, Chenhui Sui, Yanan Refai, Mohamed Irfan Sartori, Massimo Durandau, Guillaume Kumar, Vikash Caggiano, Vittorio |
| author_facet | Wang, Cheryl Tan, Chun Kwang Hodossy, Balint K. Lyu, Eric Guo, Jun Zhao, Wentao Liu, Huaping Li, Chengkun Simos, Merkourios Ziliotto, Bianca Mathis, Alexander Liu, Siyuan Chen, Jiahao Zhong, Shanlin Jiang, Bo Song, Ci Zhu, Yaoye Zuo, Chenhui Sui, Yanan Refai, Mohamed Irfan Sartori, Massimo Durandau, Guillaume Kumar, Vikash Caggiano, Vittorio |
| contents | 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, 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 and a trunk; and a soccer penalty kick using a biomechanic model of legs and a trunk. 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. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15650 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence Wang, Cheryl Tan, Chun Kwang Hodossy, Balint K. Lyu, Eric Guo, Jun Zhao, Wentao Liu, Huaping Li, Chengkun Simos, Merkourios Ziliotto, Bianca Mathis, Alexander Liu, Siyuan Chen, Jiahao Zhong, Shanlin Jiang, Bo Song, Ci Zhu, Yaoye Zuo, Chenhui Sui, Yanan Refai, Mohamed Irfan Sartori, Massimo Durandau, Guillaume Kumar, Vikash Caggiano, Vittorio Robotics 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, 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 and a trunk; and a soccer penalty kick using a biomechanic model of legs and a trunk. 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. 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. |
| title | MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.15650 |