publications

2026

1. A generalist policy for muscle-driven control across individual musculoskeletal models

   Bozhao Wang^†, Yinglei Zhu^†, Zhengqing Li, Zhiyuan Zhang, Weiming Kang, Yiheng He, XinJun Liu, Senhao Hou^*, and 2 more authors

   arXiv, 2026

   Differences in musculoskeletal morphology challenge policy reuse across individual models. Here we present MyoAcross, a framework that combines individual musculoskeletal model construction, morphology-conditioned policy learning and virtual model augmentation to enable muscle-driven reference-motion tracking without individual fine-tuning. Across 31 held-out individual models, MyoAcross achieves 99.24% completion and reduces tracking error by 22.60% relative to an unconditioned multilayer perceptron. Policy reuse extends to models of 22 held-out individuals constructed from single-image shape estimates, measured height and body mass, and, with asymmetry augmentation, to models with unilateral lower-limb shortening. Comparisons with measurements from 120 additional individuals show agreement in vertical ground reaction force waveforms and channel-dependent agreement between simulated muscle activations and electromyography. These results establish a common control framework for comparative simulations of movement across individual musculoskeletal models.

   @misc{wang2026myoacross,
     title = {A generalist policy for muscle-driven control across individual musculoskeletal models},
     author = {Wang, Bozhao and Zhu, Yinglei and Li, Zhengqing and Zhang, Zhiyuan and Kang, Weiming and He, Yiheng and Liu, XinJun and Hou, Senhao and Zhao, Huichan and Tang, Xiaoqiang},
     year = {2026},
     eprint = {2610.36535},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     url = {https://arxiv.org/abs/2610.36535},
   }

2025

1. EquiMus: Energy-Equivalent Dynamic Modeling and Simulation of Musculoskeletal Robots Driven by Linear Elastic Actuators

   Yinglei Zhu^†, Xuguang Dong, Qiyao Wang, Qi Shao, Fugui Xie, Xinjun Liu, and Huichan Zhao^*

   RA-L, 2025

   Dynamic modeling and control are critical for unleashing soft robots’ potential, yet remain challenging due to their complex constitutive behaviors and real-world operating conditions. Bio-inspired musculoskeletal robots, which integrate rigid skeletons with soft actuators, combine high load-bearing capacity with inherent flexibility. Although actuation dynamics have been studied through experimental methods and surrogate models, accurate and effective modeling and simulation remain a significant challenge, especially for large-scale hybrid rigid–soft robots with continuously distributed mass, kinematic loops, and diverse motion modes. To address these challenges, we propose EquiMus, an energy-equivalent dynamic modeling framework and MuJoCo-based simulation for musculoskeletal rigid–soft hybrid robots with linear elastic actuators. The equivalence and effectiveness of the proposed approach are validated and examined through both simulations and real-world experiments on a bionic robotic leg. EquiMus further demonstrates its utility for downstream tasks, including controller design and learning-based control strategies.

   @article{zhu2025eequimus,
     author = {Zhu, Yinglei and Dong, Xuguang and Wang, Qiyao and Shao, Qi and Xie, Fugui and Liu, Xinjun and Zhao, Huichan},
     journal = {IEEE Robotics and Automation Letters},
     title = {EquiMus: Energy-Equivalent Dynamic Modeling and Simulation of Musculoskeletal Robots Driven by Linear Elastic Actuators},
     year = {2025},
     volume = {10},
     number = {12},
     pages = {12668-12675},
     keywords = {Actuators;Soft robotics;Musculoskeletal system;Dynamics;Robot kinematics;Computational modeling;Load modeling;Data models;Damping;Bio-inspired robotics;Animation;Modeling;control;and learning for soft robots;biologically-inspired robots;dynamics;simulation and animation},
     doi = {10.1109/LRA.2025.3621980},
   }

2. Design and Control of a Musculoskeletal Bionic Leg With Optimized and Sensorized Soft Artificial Muscles

   Xuguang Dong^†, Yixin Wang, Jingyi Zhou, Xin An, Yinglei Zhu, Fugui Xie, Xin-Jun Liu, and Huichan Zhao^*

   T-RO, 2025

   The development of high-performance bionic legged robots can benefit from the continued advancements in various actuation methods, such as artificial muscles. This work presents a musculoskeletal bionic leg driven by fluidic elastomer actuators (FEAs), showcasing their potential as artificial muscles for legged robots. Our approach integrates three key innovations: First, we established a mechanics model using thin plate theory to optimize the bellows shell structure of the FEAs, achieving high force output while maintaining inherent compliance. Second, we developed a lightweight embedded optoelectronic sensing system that enables closed-loop control without significantly increasing mass. Third, we designed a two-joint leg in the sagittal plane that utilizes a bionic configuration incorporating both monoarticular and biarticular FEAs. The leg demonstrated robust performance across various tasks including extreme positional movements, load-bearing squats supporting up to 2.45 times its body weight, vertical jumping with 147 mm ground clearance, and stable walking. Notably, our embedded sensing system successfully detected ground contact states without additional foot sensors, enabling reliable gait control while minimizing complexity and weight. The experimental results validate both the mechanical capabilities of the optimized FEAs and their controllability through embedded sensing, laying a foundation for developing full legged robots with muscle-like actuation.

   @article{10989779,
     author = {Dong, Xuguang and Wang, Yixin and Zhou, Jingyi and An, Xin and Zhu, Yinglei and Xie, Fugui and Liu, Xin-Jun and Zhao, Huichan},
     journal = {IEEE Transactions on Robotics},
     title = {Design and Control of a Musculoskeletal Bionic Leg With Optimized and Sensorized Soft Artificial Muscles},
     year = {2025},
     volume = {41},
     number = {},
     pages = {3402-3422},
     keywords = {Legged locomotion;Robots;Robot sensing systems;Sensors;Actuators;Force;Artificial muscles;Optical sensors;Bellows;Optical fiber sensors;Biologically-inspired robots;fluidic elastomer actuator (FEA);legged robotics;soft sensors and actuators},
     doi = {10.1109/TRO.2025.3567801},
   }

2024

1. Whleaper: A 10-DOF Flexible Bipedal Wheeled Robot

   Yinglei Zhu^†, Sixiao He^†, Zhenghao Qi, Zhuoyuan Yong, Yihua Qin, and Jianyu Chen^*

   IROS, 2024

   Wheel-legged robots combine the advantages of both wheeled robots and legged robots, offering versatile locomotion capabilities with excellent stability on challenging terrains and high efficiency on flat surfaces. However, existing wheel-legged robots typically have limited hip joint mobility compared to humans, while hip joint plays a crucial role in locomotion. In this paper, we introduce Whleaper, a novel 10-degree-of-freedom (DOF) bipedal wheeled robot, with 3 DOFs at the hip of each leg. Its humanoid joint design enables adaptable motion in complex scenarios, ensuring stability and flexibility. This paper introduces the details of Whleaper, with a focus on innovative mechanical design, control algorithms and system implementation. Firstly, stability stems from the increased DOFs at the hip, which expand the range of possible postures and improve the robot’s foot-ground contact. Secondly, the extra DOFs also augment its mobility. During walking or sliding, more complex movements can be adopted to execute obstacle avoidance tasks. Thirdly, we utilize two control algorithms to implement multimodal motion for walking and sliding. By controlling specific DOFs of the robot, we conducted a series of simulations and practical experiments, demonstrating that a high-DOF hip joint design can effectively enhance the stability and flexibility of wheel-legged robots. Whleaper shows its capability to perform actions such as squatting, obstacle avoidance sliding, and rapid turning in real-world scenarios.

   @inproceedings{zhu2024whleaper,
     title = {Whleaper: A 10-DOF Flexible Bipedal Wheeled Robot},
     author = {Zhu, Yinglei and He, Sixiao and Qi, Zhenghao and Yong, Zhuoyuan and Qin, Yihua and Chen, Jianyu},
     booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
     pages = {11272-11277},
     year = {2024},
     organization = {IEEE},
     doi = {10.1109/IROS58592.2024.10801355},
   }