문희승 연구실

Data-driven methods for inferring user intentions in 3D environments using biomechanical simulation to produce synthetic motion data, capturing movement-related factors such as limb configurations and motor noise.

Creating rational agents with human-like perception and motor abilities trained via reinforcement learning to replicate task speed, accuracy, and motor variability of human users.

Enhancing target selection in virtual reality environments through inference models that reduce selection error and completion time in challenging 3D pointing conditions.