  We present a fast learning-based inertial parameters estimation framework
capable of understanding the dynamics of an unknown object to enable a humanoid
(or manipulator) to more safely and accurately interact with its surrounding
environments. Unlike most relevant literature, our framework doesn't require to
use of a force/torque sensor, vision system, and a long-horizon trajectory. To
achieve fast inertia parameter estimation, a time-series data-driven regression
model is utilized rather than solving a constrained optimization problem. Due
to the challenge of obtaining a large number of the ground truth of inertia
parameters in the real world, we acquire a reliable dataset in a high-fidelity
simulation that is developed using a real-to-sim adaptation. The adaptation
method we introduced consists of two components: 1) \textit{Robot System
Identification} and 2) \textit{Gaussian Processes}. We demonstrate our method
with a 4-DOF single manipulator of a wheeled humanoid robot, SATYRR. Results
show that our method can identify the inertial parameters of various unknown
objects quickly while maintaining sufficient accuracy compared to other
methods. Manipulation and locomotion experiments were also carried out to show
the benefit of using the estimated inertia parameters from control perspective.
