  Robot planning is the process of selecting a sequence of actions that
optimize for a task specific objective. The optimal solutions to such tasks are
heavily influenced by the implicit structure in the environment, i.e. the
configuration of objects in the world. State-of-the-art planning approaches,
however, do not exploit this structure, thereby expending valuable effort
searching the action space instead of focusing on potentially good actions. In
this paper, we address the problem of enabling planners to adapt their search
strategies by inferring such good actions in an efficient manner using only the
information uncovered by the search up until that time. We formulate this as a
problem of sequential decision making under uncertainty where at a given
iteration a planning policy must map the state of the search to a planning
action. Unfortunately, the training process for such partial information based
policies is slow to converge and susceptible to poor local minima. Our key
insight is that if we could fully observe the underlying world map, we would
easily be able to disambiguate between good and bad actions. We hence present a
novel data-driven imitation learning framework to efficiently train planning
policies by imitating a clairvoyant oracle - an oracle that at train time has
full knowledge about the world map and can compute optimal decisions. We
leverage the fact that for planning problems, such oracles can be efficiently
computed and derive performance guarantees for the learnt policy. We examine
two important domains that rely on partial information based policies -
informative path planning and search based motion planning. We validate the
approach on a spectrum of environments for both problem domains, including
experiments on a real UAV, and show that the learnt policy consistently
outperforms state-of-the-art algorithms.

Robotics