Robotics: Science and Systems XVII

NeRP: Neural Rearrangement Planning for Unknown Objects

Ahmed H Qureshi, Arsalan Mousavian, Chris Paxton, Michael Yip, Dieter Fox


Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such; the rearrangement of objects has been noted to be an important benchmark for AI capabilities in recent years. We propose NeRP (Neural Rearrangement Planning); a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects; that is trained on simulation data; and generalizes to the real world. We compare NeRP to several naive and model-based baselines; demonstrating that our approach is measurably better and can efficiently arrange unseen objects in fewer steps and with less planning time. Finally; we demonstrate it on several challenging rearrangement problems in the real world.



    AUTHOR    = {Ahmed H Qureshi AND Arsalan Mousavian AND Chris Paxton AND Michael Yip AND Dieter Fox}, 
    TITLE     = {{NeRP: Neural Rearrangement Planning for Unknown Objects}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.072}