  Models used in modern planning problems to simulate outcomes of real world
action executions are becoming increasingly complex, ranging from simulators
that do physics-based reasoning to precomputed analytical motion primitives.
However, robots operating in the real world often face situations not modeled
by these models before execution. This imperfect modeling can lead to highly
suboptimal or even incomplete behavior during execution. In this paper, we
propose CMAX an approach for interleaving planning and execution. CMAX adapts
its planning strategy online during real-world execution to account for any
discrepancies in dynamics during planning, without requiring updates to the
dynamics of the model. This is achieved by biasing the planner away from
transitions whose dynamics are discovered to be inaccurately modeled, thereby
leading to robot behavior that tries to complete the task despite having an
inaccurate model. We provide provable guarantees on the completeness and
efficiency of the proposed planning and execution framework under specific
assumptions on the model, for both small and large state spaces. Our approach
CMAX is shown to be efficient empirically in simulated robotic tasks including
4D planar pushing, and in real robotic experiments using PR2 involving a 3D
pick-and-place task where the mass of the object is incorrectly modeled, and a
7D arm planning task where one of the joints is not operational leading to
discrepancy in dynamics. The video of our physical robot experiments can be
found at https://youtu.be/eQmAeWIhjO8
