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We present a novel method enabling robots to quickly learn to manipulateobjects by leveraging a motion planner to generate "expert" trainingtrajectories from a small amount of human-labeled data. In contrast to thetraditional sense-plan-act cycle, we propose a deep learning architecture andtraining regimen called PtPNet that can estimate effective end-effectortrajectories for manipulation directly from a single RGB-D image of an object.Additionally, we present a data collection and augmentation pipeline thatenables the automatic generation of large numbers (millions) of training imageand trajectory examples with almost no human labeling effort.  We demonstrate our approach in a non-prehensile tool-based manipulation task,specifically picking up shoes with a hook. In hardware experiments, PtPNetgenerates motion plans (open-loop trajectories) that reliably (89% success over189 trials) pick up four very different shoes from a range of positions andorientations, and reliably picks up a shoe it has never seen before. Comparedwith a traditional sense-plan-act paradigm, our system has the advantages ofoperating on sparse information (single RGB-D frame), producing high-qualitytrajectories much faster than the "expert" planner (300ms versus severalseconds), and generalizing effectively to previously unseen shoes.

Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner