2017
Computationally Efficient Rigid-Body Gaussian Process for Motion Dynamics
RA-L 2017
In this letter, we address the modeling and learning of complex nonlinear rigid-body motions employing Gaussian processes. As the common procedure of using Euler angles in the Gaussian process results in inaccurate predictions for large rotations, we represent the input data by axis-angle pseudovect