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arxiv: 2605.15650 · v1 · pith:6QD6O54Inew · submitted 2026-05-15 · 💻 cs.RO

MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence

Cheryl Wang , Chun Kwang Tan , Balint K. Hodossy , Eric Lyu , Jun Guo , Wentao Zhao , Huaping Liu , Chengkun Li
show 16 more authors
Merkourios Simos Bianca Ziliotto Alexander Mathis Siyuan Liu Jiahao Chen Shanlin Zhong Bo Jiang Ci Song Yaoye Zhu Chenhui Zuo Yanan Sui Mohamed Irfan Refai Massimo Sartori Guillaume Durandau Vikash Kumar Vittorio Caggiano
This is my paper

Pith reviewed 2026-05-20 19:16 UTC · model grok-4.3

classification 💻 cs.RO
keywords MyoChallengemusculoskeletal modelmotor control benchmarktable tennissoccer penalty kickMyoSuitephysics simulationathletic intelligence
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The pith

MyoChallenge 2025 sets up standardized simulation tasks with realistic muscle models to test AI control of athletic movements in table tennis and soccer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces MyoChallenge 2025 as a new benchmark competition that places machine learning algorithms in control of high-fidelity musculoskeletal models inside a physics simulator. One track requires an upper-limb model to play table tennis rallies while the other requires a lower-limb model to execute soccer penalty kicks. The authors argue that these tasks, built inside the open-source MyoSuite framework, create a shared, reproducible platform that links machine learning, biomechanics, sports science, and neuroscience. The competition drew nearly 70 teams and produced several new control methods that combine motion planning, behavior cloning, and muscle synergy ideas. The central goal is to close the gap between current AI systems and the rapid, coordinated physical intelligence that human athletes display.

Core claim

The authors establish MyoChallenge 2025 as a benchmark that integrates physiologically realistic musculoskeletal models and standardized sports tasks into the MyoSuite simulation framework, thereby providing a reusable testbed for evaluating and advancing machine-learning algorithms on human-like motor control and athletic agility.

What carries the argument

Two biomechanically detailed musculoskeletal models inside a physics simulator—one upper-limb model for table-tennis rallies and one lower-limb-plus-trunk model for soccer penalty kicks—hosted in the open-source MyoSuite framework and used to score control algorithms on coordination, agility, and force production.

If this is right

  • New control algorithms that combine physics-based planners, on-policy cloning, hierarchical planning, and muscle synergies can be developed and compared on the same tasks.
  • Standardized tasks and models become a shared resource that supports repeated experiments across machine learning, biomechanics, sports science, and neuroscience.
  • The benchmark accelerates the creation of AI systems capable of rapid decision-making and precise physical execution in dynamic environments.
  • Global participation in the competition generates a public collection of high-performing controllers for further analysis and improvement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Success on these tasks could supply training signals for robotic systems that need human-like agility, such as prosthetic limbs or assistive exoskeletons.
  • The collected simulation trajectories may reveal coordination patterns that are difficult to observe directly in living athletes.
  • The same modeling approach could be extended to other skilled movements such as gymnastics or rehabilitation exercises.

Load-bearing premise

The simulator's muscle and joint models capture enough of real human coordination, agility, and force output for performance in the virtual tasks to transfer to actual athletic behavior.

What would settle it

A side-by-side comparison in which the highest-scoring simulation policies, when transferred to physical human subjects or high-resolution motion-capture data, produce measurably lower accuracy, speed, or coordination scores than the same policies achieve inside the simulator.

Figures

Figures reproduced from arXiv: 2605.15650 by Alexander Mathis, Balint K. Hodossy, Bianca Ziliotto, Bo Jiang, Chengkun Li, Chenhui Zuo, Cheryl Wang, Chun Kwang Tan, Ci Song, Eric Lyu, Guillaume Durandau, Huaping Liu, Jiahao Chen, Jun Guo, Massimo Sartori, Merkourios Simos, Mohamed Irfan Refai, Shanlin Zhong, Siyuan Liu, Vikash Kumar, Vittorio Caggiano, Wentao Zhao, Yanan Sui, Yaoye Zhu.

Figure 1
Figure 1. Figure 1: Two tracks of MyoChallenge 2025. A. The Table Tennis track in which an agent is required to hold a paddle and return an incoming table tennis ball B. The Soccer track where the agent needs to score a penalty goal with or without a goalie. phase, submissions were evaluated using static environments with no task variations, encouraging participants of all skill levels to test early solutions. Teams scoring a… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Framework overview. The policy takes as input current observation and high￾level commands from the planner, and generates the target joint positions, which are subsequently translated into muscle signals through the Kinematics-based Muscle Actuation Controller. (b) Kinematics-based Muscle Actuation Controller. Through proving the differential flatness, Diff￾Muscle integrates forward kinematics and a PD… view at source ↗
Figure 3
Figure 3. Figure 3: BioSyn neural controller. The controller is structured according to the musculoskeletal [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of the agent executing (a) a forehand strike and (b) a backhand strike. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overview of Servette MyoClub training pipeline. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Task visualization. Top: Ball-reaching performance at from-scratch initialization. Middle: Zero-shot ball-reaching performance by KINESIS. Bottom: Final ball kicking performance after on￾policy behavior cloning. Key Solution Insight The winning solution decomposed the penalty kick task into two stages (see [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Schematic diagram of online behavior cloning. [PITH_FULL_IMAGE:figures/full_fig_p025_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Example video conversion ability of MyoChallenge Bot. [PITH_FULL_IMAGE:figures/full_fig_p028_8.png] view at source ↗
read the original 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, 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript announces MyoChallenge 2025, the fourth iteration of the MyoChallenge series at NeurIPS 2025. It describes two tracks using high-fidelity musculoskeletal models in the MyoSuite physics simulator: an upper-limb table-tennis rally task (arm, hand, and trunk) and a lower-limb soccer penalty-kick task (legs and trunk). The paper reports participation by nearly 70 teams with over 560 submissions and notes the emergence of control methods such as physics-based motion planners, on-policy behavior cloning, hierarchical planning, and muscle synergies. It claims that the standardized, open-source tasks and physiologically realistic models will serve as a reproducible testbed to accelerate interdisciplinary research across machine learning, biomechanics, sports science, and neuroscience.

Significance. If adopted, the benchmark could provide a valuable, reproducible platform for evaluating motor-control algorithms on complex, multi-joint musculoskeletal systems. The open-source MyoSuite integration, the scale of community participation, and the explicit focus on physiologically inspired models are concrete strengths that could foster cross-disciplinary work. The prospective utility claim is common for benchmark papers and rests on the platform's accessibility rather than new empirical findings.

major comments (1)
  1. [Abstract] Abstract, paragraph describing the two tracks: the claim that the models 'sufficiently capture the coordination, agility, and force production of real human athletic performance' is presented without any cited validation metrics, error analysis, or comparison to human kinematic or kinetic data. This assumption is load-bearing for the central assertion that the benchmark bridges the gap in biological understanding.
minor comments (2)
  1. [Abstract] The project-page URL contains a double slash (https://www.myosuite.org//myochallenge/myochallenge-2025); this should be corrected for accessibility.
  2. The full text should include at least one table or figure summarizing baseline performance metrics or participation statistics to allow readers to gauge the current state of the submitted algorithms.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and recommendation of minor revision. We address the single major comment below and will incorporate changes to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract, paragraph describing the two tracks: the claim that the models 'sufficiently capture the coordination, agility, and force production of real human athletic performance' is presented without any cited validation metrics, error analysis, or comparison to human kinematic or kinetic data. This assumption is load-bearing for the central assertion that the benchmark bridges the gap in biological understanding.

    Authors: We agree that the abstract's phrasing regarding model fidelity would benefit from explicit support. The musculoskeletal models are the established high-fidelity models from the MyoSuite framework (based on prior biomechanical studies), but this manuscript does not include direct validation metrics or new comparisons to human data. We will revise the abstract to cite the relevant MyoSuite validation papers that report kinematic and kinetic comparisons, and adjust the language to 'designed to approximate' rather than 'sufficiently capture' to avoid overstatement while preserving the benchmark's motivation. revision: yes

Circularity Check

0 steps flagged

No significant circularity in benchmark announcement

full rationale

The document is a competition announcement describing standardized tasks, physiologically realistic models in MyoSuite, two tracks (table tennis and soccer), participation numbers, and developed algorithms. It makes a prospective claim that the benchmark accelerates interdisciplinary research but contains no derivations, equations, fitted parameters, predictions, or load-bearing self-citations. No step reduces to its own inputs by construction; the utility statement is forward-looking and common for benchmark papers. The derivation chain is absent, rendering circularity analysis inapplicable.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a benchmark announcement rather than a derivation; it introduces no free parameters, mathematical axioms, or new postulated entities.

pith-pipeline@v0.9.0 · 5918 in / 1014 out tokens · 43228 ms · 2026-05-20T19:16:30.100794+00:00 · methodology

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