MyoChallenge 2024: A New Benchmark for Physiological Dexterity and Agility in Bionic Humans

Cheryl Wang, Chun Kwang Tan, Balint K Hodossy, Shirui Lyu, Pierre Schumacher, James Heald, Kai Biegun, Samo Hromadka, Maneesh Sahani, Gunwoo Park, Beomsoo Shin, JongHyun Park, SEUNGBUM KOO, Chenhui Zuo, Chengtian Ma, Yanan Sui, Nicklas Hansen, Stone Tao, Yuan Gao, Hao Su et al. (6 additional authors not shown)

Keywords: embodied intelligence, reinforcement learning, neuromusculoskeletal control, manipulation, locomotion, challenge

TL;DR: MyoChallenge 2024 at NeurIPS introduced a benchmark for coordinated control of biological and prosthetic limbs through two tasks, manipulation and locomotion, using physiologically realistic models.

Abstract: Recent advancements in bionic prosthetic technology offer transformative opportunities to restore mobility and functionality for individuals with missing limbs. Users of bionic limbs, or bionic humans, learn to seamlessly integrate prosthetic extensions into their motor repertoire, regaining critical motor abilities. The remarkable movement generalization and environmental adaptability demonstrated by these individuals highlight motor intelligence capabilities unmatched by current artificial intelligence systems. Addressing these limitations, MyoChallenge '24 at NeurIPS 2024 established a benchmark for human-robot coordination with an emphasis on joint control of both biological and mechanical limbs. The competition featured two distinct tracks: a manipulation task utilizing the myoMPL model, integrating a virtual biological arm and the Modular Prosthetic Limb (MPL) for a passover task; and a locomotion task using the novel myoOSL model, combining a bilateral virtual biological leg with a trans-femoral amputation and the Open Source Leg (OSL) to navigate varied terrains. Marking the third iteration of the MyoChallenge, the event attracted over 50 teams with more than 290 submissions all around the globe, with diverse participants ranging from independent researchers to high school students. The competition facilitated the development of several state-of-the-art control algorithms for bionic musculoskeletal systems, leveraging techniques such as imitation learning, muscle synergy, and model-based reinforcement learning that significantly surpassed our proposed baseline performance by a factor of 10. By providing the open-source simulation framework of MyoSuite, standardized tasks, and physiologically realistic models, MyoChallenge serves as a reproducible testbed and benchmark for bridging ML and biomechanics. The competition website is featured here: https://sites.google.com/view/myosuite/myochallenge/myochallenge-2024.

Code URL: https://github.com/MyoHub/myosuite

Primary Area: Data for Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)

Submission Number: 1541