  In this work, we introduce PokeRRT, a novel motion planning algorithm that
demonstrates poking as an effective non-prehensile manipulation skill to enable
fast manipulation of objects and increase the size of a robot's reachable
workspace. We showcase poking as a failure recovery tactic used synergistically
with pick-and-place for resiliency in cases where pick-and-place initially
fails or is unachievable. Our experiments demonstrate the efficiency of the
proposed framework in planning object trajectories using poking manipulation in
uncluttered and cluttered environments. In addition to quantitatively and
qualitatively demonstrating the adaptability of PokeRRT to different scenarios
in both simulation and real-world settings, our results show the advantages of
poking over pushing and grasping in terms of success rate and task time.
