ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation

Goal. eefin which the high-level policy learns the sub-goal for the endeffector, and the low-level policy learns how to move the endeffector conditioned on the predicted goal.We demonstrate that this hierarchical approach achieves much better objectlevel generalization compared to the non-hierarchical version.We further propose a novel weighted displacement model for the high-level policy that grounds the prediction into the existing 3D structure of the scene, outperforming alternative policy representations.We show that our learned policy can zero-shot transfer to three different real robot settings: a fixed table-top Franka arm across two different labs, and an X-Arm on a mobile base, opening multiple unseen articulated objects across two labs, real lounges, and kitchens.Videos and code can be found on our project website: https://articubot.github.io/.