Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics

TL;DR

This paper introduces a scalable simulation framework using a musculoskeletal model and reinforcement learning to analyze and optimize human-robot interactions, providing internal biomechanical insights for better design.

Contribution

It presents a novel simulation-based approach that combines a full-body musculoskeletal model with reinforcement learning to evaluate and optimize physical human-robot interactions.

Findings

Improved joint alignment in exoskeletons

Reduced contact forces in simulations

Enhanced design exploration capabilities

Abstract

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.