# Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI

arXiv: https://arxiv.org/abs/2508.16581 · HTML: https://mlsanity.com/paper/2508.16581/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)](https://mlsanity.com/paper/2112.15422/) (2021)
- [Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking](https://mlsanity.com/paper/2406.03704/) (2024)
- [Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning](https://mlsanity.com/paper/2002.02697/) (2020)
- [Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity](https://mlsanity.com/paper/2011.07105/) (2020)
- [SIM2VR: Towards Automated Biomechanical Testing in VR](https://mlsanity.com/paper/2404.17695/) (2024)
- [What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation](https://mlsanity.com/paper/2503.02571/) (2025)

## Built on by (most cited first, 3 of 3)

- [Log2Motion: Biomechanical Motion Synthesis from Touch Logs](https://mlsanity.com/paper/2601.21043/) (2026)
- [MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning](https://mlsanity.com/paper/2602.15245/) (2026)
- [Designing Fatigue-Aware VR Interfaces via Biomechanical Models](https://mlsanity.com/paper/2603.26031/) (2026)

## Abstract (arXiv)

Biomechanical forward simulation holds great potential for HCI, enabling the generation of human-like movements in interactive tasks. However, training biomechanical models with reinforcement learning is challenging, particularly for precise and dexterous movements like those required for touchscreen interactions on mobile devices. Current approaches are limited in their interaction fidelity, require restricting the underlying biomechanical model to reduce complexity, and do not generalize well. In this work, we propose practical improvements to training routines that reduce training time, increase interaction fidelity beyond existing methods, and enable the use of more complex biomechanical models. Using a touchscreen pointing task, we demonstrate that curriculum learning, action masking, more complex network configurations, and simple adjustments to the simulation environment can significantly improve the agent's ability to learn accurate touch behavior. Our work provides HCI researchers with practical tips and training routines for developing better biomechanical models of human-like interaction fidelity.