53 citations · 123 across the 12 of their papers we have counts for
4 papers · 1 filter
Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning
Charlotte Beylier, Hannah Selder, Arthur Fleig +2
The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological framework for analyzing the lear…
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…
Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI
Arthur Fleig, Florian Fischer, Markus Klar +5
Computational models of how users perceive and act within a virtual or physical environment offer enormous potential for the understanding and design of user interactions. Cognitio…
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…