  Acquiring dynamics is an essential topic in robot learning, but up-to-date
methods, such as dynamics randomization, need to restart to check nominal
parameters, generate simulation data, and train networks whenever they face
different robots. To improve it, we novelly investigate general robot dynamics,
its inverse models, and Gen2Real, which means transferring to reality. Our
motivations are to build a model that learns the intrinsic dynamics of various
robots and lower the threshold of dynamics learning by enabling an amateur to
obtain robot models without being trapped in details. This paper achieves the
"generality" by randomizing dynamics parameters, topology configurations, and
model dimensions, which in sequence cover the property, the connection, and the
number of robot links. A structure modified from GPT is applied to access the
pre-training model of general dynamics. We also study various inverse models of
dynamics to facilitate different applications. We step further to investigate a
new concept, "Gen2Real", to transfer simulated, general models to physical,
specific robots. Simulation and experiment results demonstrate the validity of
the proposed models and method.\footnote{ These authors contribute equally.
