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[2602.15245] MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning
Summary
MyoInteract is a novel framework that simplifies the prototyping of biomechanical HCI tasks using reinforcement learning, significantly reducing setup and training times for designers.
Why It Matters
This framework addresses the usability and interpretability challenges in biomechanical reinforcement learning, making it accessible for designers and accelerating research in human-computer interaction. By lowering barriers to entry, it encourages innovation in HCI design and application.
Key Takeaways
- MyoInteract enables rapid prototyping of biomechanical tasks with a user-friendly GUI.
- It reduces training times for muscle-actuated simulations by up to 98%.
- The framework allows novices to successfully set up and assess user movements in a single session.
- It transforms a complex, expert-driven process into an accessible workflow.
- MyoInteract fosters faster iteration cycles in HCI biomechanics research.
Computer Science > Human-Computer Interaction arXiv:2602.15245 (cs) [Submitted on 16 Feb 2026] Title:MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning Authors:Ankit Bhattarai, Hannah Selder, Florian Fischer, Arthur Fleig, Per Ola Kristensson View a PDF of the paper titled MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning, by Ankit Bhattarai and 4 other authors View PDF HTML (experimental) 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 iteratio...
