D. Nguyen

Physics-Informed Learning for Forward Kinematics of Robotic Manipulators Under Uncertainty

Experimental results demonstrate that the learned uncertainty consistently bounds the observed end-effector variability for both translational and rotational components, while achieving improved probabilistic calibration, reliable empirical coverage, and significantly lower inference cost.