mlsanityData to 7 Oct 2026

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

arXiv 2602.15245 ↗First posted 16 Feb 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 68 references that are in the map. First posted on arXiv 16 Feb 2026.

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24 of 68 references.

References · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
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12 more
  1. 2025Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotioncited by 6
  2. 2024Active Inference and Human--Computer Interactioncited by 4
  3. 2021Optimal Feedback Control for Modeling Human-Computer Interactioncited by 3
  4. 2022Simulating Interaction Movements via Model Predictive Controlcited by 3
  5. 2025What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulationcited by 3
  6. 2025Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCIcited by 3
  7. 2025Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCIcited by 3
  8. 2024NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactionscited by 2
  9. 2025AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended Realitycited by 2
  10. 2024Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirementscited by 1
  11. 2024Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJXcited by 1
  12. 2025Redefining Affordance via Computational Rationalitycited by 1

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