  The functional demands of robotic systems often require completing various
tasks or behaviors under the effect of disturbances or uncertain environments.
Of increasing interest is the autonomy for dynamic robots, such as multirotors,
motor vehicles, and legged platforms. Here, disturbances and environmental
conditions can have significant impact on the successful performance of the
individual dynamic behaviors, referred to as "motion primitives". Despite this,
robustness can be achieved by switching to and transitioning through suitable
motion primitives. This paper contributes such a method by presenting an
abstraction of the motion primitive dynamics and a corresponding "motion
primitive transfer function". From this, a mixed discrete and continuous
"motion primitive graph" is constructed, and an algorithm capable of online
search of this graph is detailed. The result is a framework capable of
realizing holistic robustness on dynamic systems. This is experimentally
demonstrated for a set of motion primitives on a quadrupedal robot, subject to
various environmental and intentional disturbances.
