most citedDemystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI

2 citations · 2 across the 1 of their papers we have counts for

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2 papers

cs.HC2025★ 2 cited

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…

cs.HC2025

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…