Hallucinating Humans for Learning Robotic Placement of Objects

While a significant body of work has been done on grasping objects, there is little prior work on placing and arranging objects in the environment. In this work, we consider placing multiple objects in complex placing areas, where neither the object nor the placing area may have been seen by the robot before. Specifically, the placements should not only be stable, but should also follow human usage preferences. We present learning and inference algorithms that consider these aspects in placing. In detail, given a set of 3D scenes containing objects, our method, based on Dirichlet process mixture models, samples human poses in each scene and learns how objects relate to those human poses. Then given a new room, our algorithm is able to select meaningful human poses and use them to determine where to place new objects. We evaluate our approach on a variety of scenes in simulation, as well as on robotic experiments.