  Humans, in comparison to robots, are remarkably adept at reaching for objects
in cluttered environments. The best existing robot planners are based on random
sampling of configuration space -- which becomes excessively high-dimensional
with large number of objects. Consequently, most planners often fail to
efficiently find object manipulation plans in such environments. We addressed
this problem by identifying high-level manipulation plans in humans, and
transferring these skills to robot planners. We used virtual reality to capture
human participants reaching for a target object on a tabletop cluttered with
obstacles. From this, we devised a qualitative representation of the task space
to abstract the decision making, irrespective of the number of obstacles. Based
on this representation, human demonstrations were segmented and used to train
decision classifiers. Using these classifiers, our planner produced a list of
waypoints in task space. These waypoints provided a high-level plan, which
could be transferred to an arbitrary robot model and used to initialise a local
trajectory optimiser. We evaluated this approach through testing on unseen
human VR data, a physics-based robot simulation, and a real robot (dataset and
code are publicly available). We found that the human-like planner outperformed
a state-of-the-art standard trajectory optimisation algorithm, and was able to
generate effective strategies for rapid planning -- irrespective of the number
of obstacles in the environment.
