# Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation

arXiv: https://arxiv.org/abs/2312.05473 · HTML: https://mlsanity.com/paper/2312.05473/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 9 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Accelerated Policy Learning with Parallel Differentiable Simulation](https://mlsanity.com/paper/2204.07137/) (2022)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation](https://mlsanity.com/paper/2307.03716/) (2023)
- [MyoDex: A Generalizable Prior for Dexterous Manipulation](https://mlsanity.com/paper/2309.03130/) (2023)
- [OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion](https://mlsanity.com/paper/2112.06061/) (2021)
- [Quantifying Performance of Bipedal Standing with Multi-channel EMG](https://mlsanity.com/paper/1711.07894/) (2017)

## Built on by (most cited first, 17 of 17)

- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes](https://mlsanity.com/paper/2412.20375/) (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)
- [Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems](https://mlsanity.com/paper/2412.20350/) (2024)
- [Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations](https://mlsanity.com/paper/2411.00128/) (2024)
- [Homogeneous Dynamics Space for Heterogeneous Humans](https://mlsanity.com/paper/2412.06146/) (2024)
- [Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations](https://mlsanity.com/paper/2506.09383/) (2025)
- [Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics](https://mlsanity.com/paper/2603.09218/) (2026)
- [Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity](https://mlsanity.com/paper/2603.29332/) (2026)
- [Human sensory-musculoskeletal modeling and control of whole-body movements](https://mlsanity.com/paper/2506.00071/) (2025)
- [Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters](https://mlsanity.com/paper/2508.13303/) (2025)
- [Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion](https://mlsanity.com/paper/2512.14329/) (2025)
- [Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models](https://mlsanity.com/paper/2512.23077/) (2025)
- [BioHuman: Learning Biomechanical Human Representations from Video](https://mlsanity.com/paper/2605.14772/) (2026)
- [UniExo: Unified Multi-Skill Policies for Musculoskeletal Locomotion and Co-Adaptive Exoskeleton Control](https://mlsanity.com/paper/2609.19690/) (2026)
- [Learning a Speed-adaptive Hip Exoskeleton Control Policy Via Sim-to-real Reinforcement Learning](https://mlsanity.com/paper/2609.28027/) (2026)

## Abstract (arXiv)

Modeling and control of the human musculoskeletal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems. However, current human musculoskeletal models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model (MS-Human-700) with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of full-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control. We validate the effectiveness of our model and algorithm in simulations with real human locomotion data. The musculoskeletal model, along with its control algorithm, will be made available to the research community to promote a deeper understanding of human motion control and better design of interactive robots. Project page: https://lnsgroup.cc/research/MS-Human-700