# MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters

arXiv: https://arxiv.org/abs/2312.07340 · HTML: https://mlsanity.com/paper/2312.07340/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 13 of 13)

- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning](https://mlsanity.com/paper/2411.16964/) (2024)
- [KINESIS: Motion Imitation for Human Musculoskeletal Locomotion](https://mlsanity.com/paper/2503.14637/) (2025)
- [Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow](https://mlsanity.com/paper/2601.19707/) (2026)
- [CBIL: Collective Behavior Imitation Learning for Fish from Real Videos](https://mlsanity.com/paper/2504.00234/) (2025)
- [Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations](https://mlsanity.com/paper/2411.00128/) (2024)
- [Arnold: A multi-task, multi-embodiment muscle transformer policy](https://mlsanity.com/paper/2508.18066/) (2025)
- [ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling](https://mlsanity.com/paper/2410.06963/) (2024)
- [Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations](https://mlsanity.com/paper/2506.09383/) (2025)
- [Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity](https://mlsanity.com/paper/2603.29332/) (2026)
- [MUSIC: Learning Muscle-Driven Dexterous Hand Control](https://mlsanity.com/paper/2604.23886/) (2026)
- [Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models](https://mlsanity.com/paper/2512.23077/) (2025)
- [TERRA: Terrain-Aware Reconstruction, Retargeting and Control for Musculoskeletal Locomotion](https://mlsanity.com/paper/2609.38653/) (2026)

## Abstract (arXiv)

In this paper, we present a simulation and control framework for generating biomechanically plausible motion for muscle-actuated characters. We incorporate a fatigue dynamics model, the 3CC-r model, into the widely-adopted Hill-type muscle model to simulate the development and recovery of fatigue in muscles, which creates a natural evolution of motion style caused by the accumulation of fatigue from prolonged activities. To address the challenging problem of controlling a musculoskeletal system with high degrees of freedom, we propose a novel muscle-space control strategy based on PD control. Our simulation and control framework facilitates the training of a generative model for muscle-based motion control, which we refer to as MuscleVAE. By leveraging the variational autoencoders (VAEs), MuscleVAE is capable of learning a rich and flexible latent representation of skills from a large unstructured motion dataset, encoding not only motion features but also muscle control and fatigue properties. We demonstrate that the MuscleVAE model can be efficiently trained using a model-based approach, resulting in the production of high-fidelity motions and enabling a variety of downstream tasks.