# What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation

arXiv: https://arxiv.org/abs/2503.02571 · HTML: https://mlsanity.com/paper/2503.02571/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2025.

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

- [Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI](https://mlsanity.com/paper/2508.16581/) (2025)
- [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)

## Abstract (arXiv)

Biomechanical models allow for diverse simulations of user movements in interaction. Their performance depends critically on the careful design of reward functions, yet the interplay between reward components and emergent behaviours remains poorly understood. We investigate what makes a model "breathe" by systematically analysing the impact of rewarding effort minimisation, task completion, and target proximity on movement trajectories. Using a choice reaction task as a test-bed, we find that a combination of completion bonus and proximity incentives is essential for task success. Effort terms are optional, but can help avoid irregularities if scaled appropriately. Our work offers practical insights for HCI designers to create realistic simulations without needing deep reinforcement learning expertise, advancing the use of simulations as a powerful tool for interaction design and evaluation in HCI.