MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning

2026-02-16

AI Summary

This work addresses the limited accessibility of reinforcement learning–based biomechanical simulation for non-expert designers, hindered by poor usability, low interpretability, and prohibitively long training times. To overcome these barriers, the authors propose a rapid prototyping framework grounded in the human action cycle model, featuring an integrated graphical user interface that unifies task specification, user modeling, and parameter configuration. By combining reinforcement learning–driven musculoskeletal simulation with efficient training algorithms and behavioral modeling, the approach reduces training time by up to 98%, compressing workflows that traditionally require days of expert effort into under one hour. In a workshop with twelve interaction designers—none of whom had prior expertise—all participants successfully completed end-to-end modeling, training, and evaluation of goal-directed movements within a single session, marking the first demonstration of a designer-accessible, closed-loop biomechanical simulation pipeline.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Intelligent User Interfaces

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

Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuated simulated users within minutes, reducing training times by up to 98%. A workshop study with 12 interaction designers revealed that MyoInteract allowed novices in biomechanical RL to successfully setup, train, and assess goal-directed user movements within a single session. By transforming biomechanical RL from a days-long expert task into an accessible hour-long workflow, this work significantly lowers barriers to entry and accelerates iteration cycles in HCI biomechanics research.

Problem

• biomechanical simulation
• reinforcement learning
• human-computer interaction
• usability
• prototyping

Innovation

• Reinforcement Learning
• Biomechanical Simulation
• Human-Computer Interaction
• Fast Prototyping
• Muscle-Actuated Models