从运动轨迹反推神经控制机制，实现动物行为的生物力学模拟。
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
- 通过神经网络学习控制物理仿真中的生物力学模型。
- 仅需少量运动数据即可准确复现真实运动轨迹。
- 适用于多种动物体型，适合神经科学建模与实验仿真。
神经系统的主要输出是运动与行为。尽管近期姿态追踪技术已普及于复杂行为分析，但仅凭运动学轨迹无法直接揭示其背后的控制机制。本文提出 MIMIC-MJX 框架，可从运动学数据中学习具有生物力学基础的神经控制策略。该框架通过训练神经控制器，在物理仿真中驱动生物力学动物模型，以重现真实的运动轨迹。实验表明，MIMIC-MJX 具有高精度、快速性及对多种动物身体模型的泛化能力，且仅需少量运动数据即可训练。该方法可用于建模运动控制策略并模拟行为实验，展现出作为神经科学研究整合建模框架的巨大潜力。
原文摘要 · Abstract (English)
The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex behavior, kinematic trajectories alone provide only indirect access to the underlying control processes. Here we present MIMIC-MJX, a framework for learning biomechanically grounded neural control policies from kinematics. MIMIC-MJX provides a platform for modeling the generative process of motor control by training neural controllers that learn to actuate biomechanical animal models in physics simulation to reproduce real kinematic trajectories. We demonstrate that our implementation is accurate, fast, and generalizable to diverse animal body models, and that it can be trained with modest amounts of motion data. MIMIC-MJX can be used to model motor control policies and simulate behavioral experiments, illustrating its potential as an integrative modeling framework for neuroscience.
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