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

TL;DR

MyoInteract is a user-friendly framework that enables rapid prototyping and training of biomechanical RL-based user models, significantly reducing setup time and making biomechanical HCI research more accessible.

Contribution

It introduces an easy-to-use GUI for biomechanical RL, enabling fast setup, training, and evaluation of simulated users, thus lowering barriers and accelerating research workflows.

Findings

Training times reduced by up to 98%.

Workshop with 12 designers showed successful task setup and evaluation.

Transforms days-long processes into hour-long workflows.

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.