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Abstract

User interface design for Virtual Reality and other embodied interaction contexts
has to carefully consider ergonomics. A common problem is that mid-air inter-
action may cause excessive arm fatigue, known as the “Gorilla arm” effect. To
predict and prevent such problems at a low cost, this thesis investigates user test-
ing of mid-air interaction without real users, utilizing biomechanically simulated
AI agents trained using deep Reinforcement Learning (RL). This is implemented
in a pointing task and four experimental conditions, demonstrating that the sim-
ulated fatigue data matches ground truth human data. Additionally, two effort
models are compared against each other: 1) instantaneous joint torques commonly
used in computer animation and robotics, and 2) the recent Three Compartment
Controller (3CC-r) model from biomechanical literature. 3CC-r yields movements
that are both more efficient and natural, whereas with instantaneous joint torques,
the RL agent can easily generate movements that are unnatural or only reach the
targets slowly and inaccurately. This thesis demonstrates that deep RL combined
with the 3CC-r provides a viable tool for predicting both interaction movements
and user experience in silico, without users.