MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
著者: Charles Y. Zhang, Yuanjia Yang, Aidan Sirbu, Elliott T. T. Abe, Emil Wärnberg, Eric J. Leonardis, Diego E. Aldarondo, Adam Lee, Aaditya Prasad, Jason Foat, Kaiwen Bian, Joshua Park, Rusham Bhatt, Vyom N. Patel, Hutton Saunders, Austin O. Barbano, Akira Nagamori, Ayesha R. Thanawalla, Kee Wui Huang, Fabian Plum, Hendrik K. Beck, Steven W. Flavell, David Labonte, Blake A. Richards, Bingni W. Brunton, Eiman Azim, Bence P. Ölveczky, Talmo D. Pereira
分類: q-bio.NC, cs.AI, cs.RO
原文アブストラクト
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.