MS-Human-700

Whole-body Human MusculoSkeletal Modeling and Control

Project Overview

The MS-Human-700 project encompasses a series of research works focused on developing comprehensive human musculoskeletal models and advanced control algorithms. This project addresses fundamental challenges in understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems.

Our research spans three key areas: musculoskeletal modeling (MS-Human-700), efficient deep reinforcement learning through dynamical synergies (DynSyn), and hierarchical model predictive control (MPC2). Together, these works provide a complete framework for simulating and controlling high-dimensional human movement.

Model Architecture

The MS-Human-700 model represents a whole-body human musculoskeletal system, featuring:

  • 90 body segments
  • 206 joints
  • 700 muscle-tendon units
  • Anatomically plausible parameters
  • MuJoCo integration

The model enables simulation of full-body dynamics and interaction with various devices, making it suitable for research in embodied intelligence, robotics, and biomechanics.

MS-Human-700 Model Visualization

Demonstration

Video Presentation

DynSyn Control Results

The MS-Human-700 model controlled by DynSyn algorithm demonstrates remarkable capabilities in both locomotion and manipulation tasks.

Locomotion Control

MS-Human-700 Locomotion Control with DynSyn

Manipulation Control

MS-Human-700 Manipulation Control with DynSyn

MPC2 Control Results

The MS-Human-700 model controlled by MPC2 algorithm demonstrates remarkable capabilities in generalizability to model changes and perturbation.

Soccer Motion Control

Adaptation Control to Model Changes

Adaptation Control to Sudden Perturbation

Research Papers

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

IEEE International Conference on Robotics and Automation (ICRA) 2024

This work introduces the MS-Human-700 model, a comprehensive musculoskeletal system with 90 body segments, 206 joints, and 700 muscle-tendon units. The paper presents a novel hierarchical deep reinforcement learning algorithm using low-dimensional representation to achieve full-body control.

Key Contributions:

  • Full-body human musculoskeletal model with 700+ muscles for simulation
  • Hierarchical control algorithm for managing high-dimensional muscle coordination
  • Validation with real human locomotion data
Project Page Code (Coming Soon) arXiv

DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems

International Conference on Machine Learning (ICML) 2024

DynSyn addresses the challenge of learning effective policies for high-dimensional, overactuated systems by introducing dynamical synergistic representations. Inspired by muscle synergies in neuromechanics, this work demonstrates how to derive synergistic representations from dynamical structures and perform task-specific adaptations.

Key Contributions:

  • Dynamical synergistic representation
  • High sample efficiency and robustness
  • Interpretable synergistic representations
  • Synergy generalizability across diverse motor tasks
Project Page Code arXiv

Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning

International Conference on Learning Representations (ICLR) 2025

MPC2 (Model Predictive Control with Morphology-aware Proportional Control) introduces a hierarchical model-based learning algorithm for zero-shot and near-real-time control of high-dimensional complex dynamical systems. The approach combines sampling-based model predictive control with morphology-aware proportional control for robust actuator coordination.

Key Contributions:

  • Hierarchical model-based control for high-dimensional systems
  • Zero-shot and near-real-time control capabilities
  • Morphology-aware proportional controller for robust coordination
  • Reduced need for manual reward engineering through black-box optimization
Project Page Code (Coming Soon) arXiv
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