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

arXiv: https://arxiv.org/abs/2602.15245 · HTML: https://mlsanity.com/paper/2602.15245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 68 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [MuJoCo Playground](https://mlsanity.com/paper/2502.08844/) (2025)
- [SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users](https://mlsanity.com/paper/2504.10157/) (2025)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [Latent Exploration for Reinforcement Learning](https://mlsanity.com/paper/2305.20065/) (2023)
- [Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity](https://mlsanity.com/paper/2011.07105/) (2020)
- [MyoDex: A Generalizable Prior for Dexterous Manipulation](https://mlsanity.com/paper/2309.03130/) (2023)
- [SIM2VR: Towards Automated Biomechanical Testing in VR](https://mlsanity.com/paper/2404.17695/) (2024)
- [Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion](https://mlsanity.com/paper/2505.20619/) (2025)
- [Active Inference and Human--Computer Interaction](https://mlsanity.com/paper/2412.14741/) (2024)
- [Optimal Feedback Control for Modeling Human-Computer Interaction](https://mlsanity.com/paper/2110.00443/) (2021)
- [Simulating Interaction Movements via Model Predictive Control](https://mlsanity.com/paper/2204.09115/) (2022)
- [What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation](https://mlsanity.com/paper/2503.02571/) (2025)
- [Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI](https://mlsanity.com/paper/2508.15727/) (2025)
- [Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI](https://mlsanity.com/paper/2508.16581/) (2025)
- [NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactions](https://mlsanity.com/paper/2406.08875/) (2024)
- [Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX](https://mlsanity.com/paper/2407.05148/) (2024)
- [AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended Reality](https://mlsanity.com/paper/2503.01011/) (2025)
- [Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements](https://mlsanity.com/paper/2402.13949/) (2024)
- [Redefining Affordance via Computational Rationality](https://mlsanity.com/paper/2501.09233/) (2025)

## 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.