4 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
Visual Bias in Simulated Users: The Impact of Luminance and Contrast on Reinforcement Learning-based Interaction
Hannah Selder, Charlotte Beylier, Nico Scherf +1
Reinforcement learning (RL) enables simulations of HCI tasks, yet their validity is questionable when performance is driven by visual rendering artifacts distinct from interaction…
MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning
Ankit Bhattarai, Hannah Selder, Florian Fischer +2
Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretabili…
Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
Hannah Selder, Florian Fischer, Per Ola Kristensson +1
Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time wi…
What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation
Hannah Selder, Florian Fischer, Per Ola Kristensson +1
Biomechanical models allow for diverse simulations of user movements in interaction. Their performance depends critically on the careful design of reward functions, yet the interpl…