Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics

2026-03-10

AI Summary

This work addresses the challenge of quantifying human–robot coupled dynamics in physical interaction scenarios, where existing robotic designs often lack direct access to internal human biomechanical states such as muscle forces and joint loads. To overcome this limitation, the authors propose a simulation-based, scalable framework that integrates an embodied human musculoskeletal model as a physiologically plausible behavioral agent. By combining reinforcement learning controllers with pretrained human motor policies, the approach enables co-optimization of robotic morphology and control strategies. The framework facilitates quantitative assessment and joint optimization of internal biomechanical metrics within the human–robot system. Evaluated on human–exoskeleton interaction tasks, it significantly improves joint alignment and reduces contact forces, demonstrating its effectiveness for data-driven, biomechanically informed design of interactive robots.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• human-robot interaction
• biomechanics
• musculoskeletal modeling
• interactive robotics
• quantitative design

Innovation

• embodied human simulation
• musculoskeletal modeling
• reinforcement learning
• human-robot interaction
• co-optimization