# MIMIC-MJX: Neuromechanical Emulation of Animal Behavior

arXiv: https://arxiv.org/abs/2511.20532 · HTML: https://mlsanity.com/paper/2511.20532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 83 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning](https://mlsanity.com/paper/2412.00568/) (2024)
- [Universal Humanoid Motion Representations for Physics-Based Control](https://mlsanity.com/paper/2310.04582/) (2023)
- [Neural probabilistic motor primitives for humanoid control](https://mlsanity.com/paper/1811.11711/) (2018)
- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [The neuroconnectionist research programme](https://mlsanity.com/paper/2209.03718/) (2022)
- [Behavior Priors for Efficient Reinforcement Learning](https://mlsanity.com/paper/2010.14274/) (2020)
- [Deep neuroethology of a virtual rodent](https://mlsanity.com/paper/1911.09451/) (2019)
- [Learning recurrent representations for hierarchical behavior modeling](https://mlsanity.com/paper/1611.00094/) (2016)
- [Differentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture](https://mlsanity.com/paper/2411.14992/) (2024)
- [KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture](https://mlsanity.com/paper/2505.13436/) (2025)
- [Scalable Option Learning in High-Throughput Environments](https://mlsanity.com/paper/2509.00338/) (2025)
- [Benchmarking Model Predictive Control and Reinforcement Learning Based Control for Legged Robot Locomotion in MuJoCo Simulation](https://mlsanity.com/paper/2501.16590/) (2025)
- [Open-Source Reinforcement Learning Environments Implemented in MuJoCo with Franka Manipulator](https://mlsanity.com/paper/2312.13788/) (2023)
- [Modular integration of neural connectomics, dynamics and biomechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans](https://mlsanity.com/paper/2504.18073/) (2025)

## Built on by (most cited first, 2 of 2)

- [Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity](https://mlsanity.com/paper/2603.29332/) (2026)
- [Massively Parallel Imitation Learning of Mouse Forelimb Musculoskeletal Reaching Dynamics](https://mlsanity.com/paper/2511.21848/) (2025)

## Abstract (arXiv)

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.