# Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics

arXiv: https://arxiv.org/abs/2603.09218 · HTML: https://mlsanity.com/paper/2603.09218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [Assistive Gym: A Physics Simulation Framework for Assistive Robotics](https://mlsanity.com/paper/1910.04700/) (2019)
- [Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation](https://mlsanity.com/paper/2312.05473/) (2023)
- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation](https://mlsanity.com/paper/2601.22550/) (2026)
- [Neuromechanical model-based adaptive control of bi-lateral ankle exoskeletons: biological joint torque and electromyogram reduction across walking conditions](https://mlsanity.com/paper/2108.00980/) (2021)

## Built on by (most cited first, 1 of 1)

- [Beyond Kinematics: Benchmarking Simulation Fidelity for Muscle-Driven Imitation Learning](https://mlsanity.com/paper/2609.21909/) (2026)

## Abstract (arXiv)

Physical interactive robotics, ranging from wearable devices to collaborative humanoid robots, require close coordination between mechanical design and control. However, evaluating interactive dynamics is challenging due to complex human biomechanics and motor responses. Traditional experiments rely on indirect metrics without measuring human internal states, such as muscle forces or joint loads. To address this issue, we develop a scalable simulation-based framework for the quantitative analysis of physical human-robot interaction. At its core is a full-body musculoskeletal model serving as a predictive surrogate for the human dynamical system. Driven by a reinforcement learning controller, it generates adaptive, physiologically grounded motor behaviors. We employ a sequential training pipeline where the pre-trained human motion control policy acts as a consistent evaluator, making large-scale design space exploration computationally tractable. By simulating the coupled human-robot system, the framework provides access to internal biomechanical metrics, offering a systematic way to concurrently co-optimize a robot's structural parameters and control policy. We demonstrate its capability in optimizing human-exoskeleton interactions, showing improved joint alignment and reduced contact forces. This work establishes embodied human simulation as a scalable paradigm for interactive robotics design.