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

This paper, MyoInteract, tackles a persistent bottleneck in Human-Computer Interaction (HCI): the gap between designing an interface and understanding how the human body will actually execute the required actions. The researchers propose a framework that uses Reinforcement Learning (RL) to rapidly prototype biomechanical simulations, specifically targeting the musculoskeletal system. By framing HCI tasks as control problems for an RL agent—which learns to move a simulated arm or hand to achieve a goal (e.g., pressing a button or gesturing)—they aim to replace slow, manual ergonomic analysis with automated, physics-grounded prediction.

The core innovation is the use of the "Human Action Cycle" (a classic HCI model by Don Norman) as a design lens. This allows the framework to break down a user’s interaction into stages: forming an intention, planning the movement, and executing it. The RL agent then learns the optimal "execution" phase, revealing not just if a task is possible, but how the body naturally adapts to constraints like fatigue, joint angles, or muscle co-contraction. This moves beyond simple collision detection or kinematic models into a richer understanding of effort and comfort.

Why it matters. For decades, HCI has been dominated by cognitive and perceptual models. We know how to measure reaction time or visual search, but we have lacked accessible tools to model the physical cost of an interaction. This is critical as we move beyond the keyboard and mouse. Consider the rise of VR/AR controllers, wearable haptics, or exoskeletons. Designers currently rely on intuition or expensive user studies to determine if a grip is fatiguing or a gesture is unnatural. MyoInteract offers a way to simulate this cost in silico, before a single prototype is built. It promises to democratize biomechanical analysis, making it a standard part of the UI/UX design process rather than a specialized subfield of sports science. Implications for AI practitioners. This is a strong signal that RL is maturing beyond game-playing and robotics into a tool for design science. For AI engineers, the key takeaway is the importance of reward shaping. The framework’s success hinges on defining a reward function that balances task completion (e.g., reaching a target) with biomechanical plausibility (e.g., minimizing joint torque or muscle activation). This requires a deep collaboration between AI experts and domain experts in physiology. Furthermore, the framework highlights the need for interpretability. An RL policy that outputs joint angles is not useful; the value is in visualizing the distribution of possible movements and the cost associated with each. Practitioners should expect to invest in visualization and logging tools to make these black-box policies explainable to designers.

Finally, this work signals a shift in the AI-HCI relationship. Instead of using AI to replace the user (e.g., autocomplete), we are using it to understand the user’s physical constraints. This opens a new market for simulation-as-a-service, where AI models are used not for generation, but for ergonomic validation.

Key Takeaways

• New Design Tool: MyoInteract uses RL to simulate how the human body physically performs HCI tasks, bridging the gap between interface design and biomechanical feasibility.
• Beyond Cognition: It addresses a blind spot in HCI by modeling the physical effort and comfort of interactions, critical for VR/AR, wearables, and assistive technology.
• RL for Validation: The framework demonstrates RL’s utility as a predictive simulation engine, not just for control, but for ergonomic analysis and design iteration.
• Cross-Disciplinary Need: Success depends on careful reward function design that blends task goals with biomechanical constraints, requiring collaboration between AI and physiology experts.