1 citations · 1 across the 8 of their papers we have counts for
8 papers
MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence
Cheryl Wang, Chun Kwang Tan, Balint K. Hodossy +21
Athletic performance represents the pinnacle of human motor intelligence, demanding rapid choices, precise control, agility, and coordinated physical execution. Replicating this se…
Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity
Yunyue Wei, Chenhui Zuo, Shanning Zhuang +3
The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inac…
Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics
Chenhui Zuo, Jinhao Xu, Michael Qian Vergnolle +1
Physical interactive robotics, ranging from wearable devices to collaborative humanoid robots, require close coordination between mechanical design and control. However, evaluating…
Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow
Yunyue Wei, Chenhui Zuo, Yanan Sui
Controlling high-dimensional systems in biological and robotic applications is challenging due to expansive state-action spaces, where effective exploration is critical. Commonly u…
Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations
Chengtian Ma, Yunyue Wei, Chenhui Zuo +2
Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of stati…
Human sensory-musculoskeletal modeling and control of whole-body movements
Chenhui Zuo, Guohao Lin, Chen Zhang +2
Coordinated human movement depends on the integration of multisensory inputs, sensorimotor transformation, and motor execution, as well as sensory feedback resulting from body-envi…
DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems
Kaibo He, Chenhui Zuo, Chengtian Ma +1
Learning an effective policy to control high-dimensional, overactuated systems is a significant challenge for deep reinforcement learning algorithms. Such control scenarios are oft…
Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation
Chenhui Zuo, Kaibo He, Jing Shao +1
Modeling and control of the human musculoskeletal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interact…