用模仿学习复现小鼠前肢抓取动作，提升神经肌肉活动预测精度
Massively Parallel Imitation Learning of Mouse Forelimb Musculoskeletal Reaching Dynamics
- 通过模仿学习将实验运动数据映射到肌肉骨骼模型
- 加入自然能量与速度约束后，模拟肌电信号更接近真实数据
- 基于JAX和Mujoco-MJX实现每秒超百万步训练，适合神经控制研究
大脑进化出高效控制身体的能力，为理解其机制需建模具身控制中的感觉运动转换。我们开发了一个以行为驱动的通用仿真平台，用于高保真建模行为动态、生物力学及神经回路架构。本文提出从神经科学实验室获取运动学数据，并构建管道在物理仿真环境中复现自然运动。采用模仿学习框架，在仿真环境中完成小鼠前肢灵巧抓取任务。当前鼠标手臂模型借助JAX和Mujoco-MJX实现GPU加速，训练速度超过每秒100万步。结果表明，引入自然能量与速度约束后，模拟的肌肉骨骼活动能更好预测真实肌电（EMG）信号。该工作证实能量与控制约束对肌肉骨骼运动控制建模至关重要。
原文摘要 · Abstract (English)
The brain has evolved to effectively control the body, and in order to understand the relationship we need to model the sensorimotor transformations underlying embodied control. As part of a coordinated effort, we are developing a general-purpose platform for behavior-driven simulation modeling high fidelity behavioral dynamics, biomechanics, and neural circuit architectures underlying embodied control. We present a pipeline for taking kinematics data from the neuroscience lab and creating a pipeline for recapitulating those natural movements in a biomechanical model. We implement a imitation learning framework to perform a dexterous forelimb reaching task with a musculoskeletal model in a simulated physics environment. The mouse arm model is currently training at faster than 1 million training steps per second due to GPU acceleration with JAX and Mujoco-MJX. We present results that indicate that adding naturalistic constraints on energy and velocity lead to simulated musculoskeletal activity that better predict real EMG signals. This work provides evidence to suggest that energy and control constraints are critical to modeling musculoskeletal motor control.
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