  Learning has propelled the cutting edge of performance in robotic control to
new heights, allowing robots to operate with high performance in conditions
that were previously unimaginable. The majority of the work, however, assumes
that the unknown parts are static or slowly changing. This limits them to
static or slowly changing environments. However, in the real world, a robot may
experience various unknown conditions. This paper presents a method to extend
an existing single mode GP-based safe learning controller to learn an
increasing number of non-linear models for the robot dynamics. We show that
this approach enables a robot to re-use past experience from a large number of
previously visited operating conditions, and to safely adapt when a new and
distinct operating condition is encountered. This allows the robot to achieve
safety and high performance in an large number of operating conditions that do
not have to be specified ahead of time. Our approach runs independently from
the controller, imposing no additional computation time on the control loop
regardless of the number of previous operating conditions considered. We
demonstrate the effectiveness of our approach in experiment on a 900\,kg ground
robot with both physical and artificial changes to its dynamics. All of our
experiments are conducted using vision for localization.
