  Multi-object manipulation problems in continuous state and action spaces can
be solved by planners that search over sampled values for the continuous
parameters of operators. The efficiency of these planners depends critically on
the effectiveness of the samplers used, but effective sampling in turn depends
on details of the robot, environment, and task. Our strategy is to learn
functions called "specializers" that generate values for continuous operator
parameters, given a state description and values for the discrete parameters.
Rather than trying to learn a single specializer for each operator from large
amounts of data on a single task, we take a modular meta-learning approach. We
train on multiple tasks and learn a variety of specializers that, on a new
task, can be quickly adapted using relatively little data -- thus, our system
"learns quickly to plan quickly" using these specializers. We validate our
approach experimentally in simulated 3D pick-and-place tasks with continuous
state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a
supplementary video.
