Balint K. Hodossy

Cheryl Wang, Chun Kwang Tan, Balint K. Hodossy et al.

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.

Balint K. Hodossy, Dario Farina

The standard engineering approach when facing uncertainty is modelling. Mixing data from a well-calibrated model with real recordings has led to breakthroughs in many applications of AI, from computer vision to autonomous driving. This type of model-based data augmentation is now beginning to show promising results in biosignal processing as well. However, while these simulated data are necessary, they are not sufficient for virtual neurophysiological experiments. Simply generating neural signals that reproduce a predetermined motor behaviour does not capture the flexibility, variability, and causal structure required to probe neural mechanisms during control tasks. In this study, we present an in silico neuromechanical model that combines a fully forward musculoskeletal simulation, reinforcement learning, and sequential, online electromyography synthesis. This framework provides not only synchronised kinematics, dynamics, and corresponding neural activity, but also explicitly models feedback and feedforward control in a virtual participant. In this way, online control problems can be represented, as the simulated human adapts its behaviour via a learned RL policy in response to a neural interface. For example, the virtual user can learn hand movements robust to perturbations or the control of a virtual gesture decoder. We illustrate the approach using a gesturing task within a biomechanical hand model, and lay the groundwork for using this technique to evaluate neural controllers, augment training datasets, and generate synthetic data for neurological conditions.