用真实肌肉结构建模犬类运动，实现精准神经控制模拟。
Motion Tracking with Muscles: Predictive Control of a Parametric Musculoskeletal Canine Model
- 基于3D肌肉网格生成可参数化犬类肌肉骨骼模型。
- 仿真肌肉激活模式与实验肌电图数据高度一致。
- 适合生物力学、机器人控制与神经科学交叉研究者使用。
我们提出一种新型犬类肌肉骨骼模型，通过精确的3D肌肉网格程序化生成。该模型配套一个基于动作捕捉的步态任务，适用于多种控制算法，并引入改进的肌肉动力学模型，以提升可微分控制框架中的收敛性。通过将模拟的肌肉激活模式与以往犬类步态研究中获取的实验肌电图（EMG）数据对比，验证了方法的有效性。本工作旨在弥合生物力学、机器人学与计算神经科学之间的差距，为研究肌肉驱动与神经肌肉控制提供可靠平台。我们计划公开完整模型及重定向的动作捕捉片段，以促进后续研究与发展。
原文摘要 · Abstract (English)
We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capture-based locomotion task compatible with a variety of control algorithms, as well as an improved muscle dynamics model designed to enhance convergence in differentiable control frameworks. We validate our approach by comparing simulated muscle activation patterns with experimentally obtained electromyography (EMG) data from previous canine locomotion studies. This work aims to bridge gaps between biomechanics, robotics, and computational neuroscience, offering a robust platform for researchers investigating muscle actuation and neuromuscular control.We plan to release the full model along with the retargeted motion capture clips to facilitate further research and development.
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