MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence

• URL: http://arxiv.org/abs/2605.15650v1
• Date: Fri, 15 May 2026 06:09:44 GMT
• Title: MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence
• Abstract summary: MyoChallenge at NeurIPS 2025 established a pioneering benchmark for motor control intelligence in sports.<n>High-fidelity musculoskeletal models within physics simulation combined with machine learning-driven algorithms.<n>Challenge attracted almost 70 teams and over 560 submissions globally.
• Score: 57.70335388371609
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SMASH: Mastering Scalable Whole-Body Skills for Humanoid Ping-Pong with Egocentric Vision [53.709632118234936]
  Methodname is a modular system for agile humanoid table tennis.<n>It unifies scalable whole-body skill learning with onboard egocentric perception.<n>We demonstrate the first humanoid table-tennis system capable of consecutive strikes using onboard sensing alone.
  arXiv  Detail & Related papers  (2026-04-01T17:11:52Z)
• Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity [11.002363474183683]
  We introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction.<n>It achieves high joint angle accuracy and body position alignment for highly dynamic tasks such as dance, cartwheel, and backflip.<n>This work establishes a tractable computational route to analyzing the specificity and diversity underlying human embodied control of movement.
  arXiv  Detail & Related papers  (2026-03-31T06:55:10Z)
• Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale [38.15316304510102]
  MuscleMimic is an open-source framework for motion imitation learning with physiologically realistic, muscle-driven models.<n>By lowering the computational and data barriers to musculoskeletal simulation, MuscleMimic enables systematic model validation across diverse dynamic movements.
  arXiv  Detail & Related papers  (2026-03-26T15:18:37Z)
• A Hierarchical, Model-Based System for High-Performance Humanoid Soccer [11.46486824528561]
  This paper presents the hardware and software innovations underlying our team's victory in the RoboCup 2024 Adult-Sized Humanoid Soccer Competition.<n>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 robustness.<n>On the software side, we develop an integrated perception and localization framework that combines stereo vision, object detection, and landmark-based fusion to provide reliable estimates of the ball, goals, teammates, and opponents.
  arXiv  Detail & Related papers  (2025-12-10T08:58:37Z)
• Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control [47.423243831156285]
  We present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control.<n>We demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data.<n>KINESIS generates muscle activity patterns that correlate well with human EMG activity.
  arXiv  Detail & Related papers  (2025-03-18T18:37:49Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation [22.925312305575183]
  We build a musculoskeletal model (MS-Human-700) with 90 body segments, 206 joints, and 700 muscle-tendon units.<n>We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control.
  arXiv  Detail & Related papers  (2023-12-09T05:42:32Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control [7.856809409051587]
  MyoSuite is a suite of physiologically accurate biomechanical models of elbow, wrist, and hand, with physical contact capabilities. We provide diverse motor-control challenges: from simple postural control to skilled hand-object interactions.
  arXiv  Detail & Related papers  (2022-05-26T20:11:23Z)
• From Motor Control to Team Play in Simulated Humanoid Football [56.86144022071756]
  We train teams of physically simulated humanoid avatars to play football in a realistic virtual environment. In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements. They then acquire mid-level football skills such as dribbling and shooting. Finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds.
  arXiv  Detail & Related papers  (2021-05-25T20:17:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.