  We propose a human-operator guided planning approach to pushing-based
manipulation in clutter. Most recent approaches to manipulation in clutter
employs randomized planning. The problem, however, remains a challenging one
where the planning times are still in the order of tens of seconds or minutes,
and the success rates are low for difficult instances of the problem. We build
on these control-based randomized planning approaches, but we investigate using
them in conjunction with human-operator input. In our framework, the human
operator supplies a high-level plan, in the form of an ordered sequence of
objects and their approximate goal positions. We present experiments in
simulation and on a real robotic setup, where we compare the success rate and
planning times of our human-in-the-loop approach with fully autonomous
sampling-based planners. We show that with a minimal amount of human input, the
low-level planner can solve the problem faster and with higher success rates.
