M-EMBER: Tackling Long-Horizon Mobile Manipulation via Factorized Domain Transfer
Bohan Wu, Roberto Martín-Martín, Li Fei-Fei
Abstract
In this paper, we propose a novel method to create visuomotor mobile manipulation solutions to long-horizon activities. We propose to leverage the recent advances in robot simulation to train robust visual solutions in simulation that can transfer to the real world. While previous works have shown success applying this procedure to autonomous visual navigation and stationary manipulation, applying it to long-horizon visuomotor mobile manipulation is still an open challenge that demands both perceptual and compositional generalization of multiple skills. In this work, we develop Mobile-EMBER, or M-EMBER, a factorized method that decomposes a long-horizon mobile manipulation activity into a repertoire of primitive visual skills, reinforcement-learns each skill in simulation, and composes these skills to a long-horizon mobile manipulation activity. On a real mobile manipulation robot, we find that M-EMBER completes a long-horizon mobile manipulation activity, cleaning_kitchen, achieving over 50% success rate. This requires successfully planning and executing five factorized, learned visual skills, in sequences of up to 48 skills long.
BibTeX
@inproceedings{icra2023_membertacklinglo,
title = {M-EMBER: Tackling Long-Horizon Mobile Manipulation via Factorized Domain Transfer},
author = {Bohan Wu and Roberto Martín-Martín and Li Fei-Fei},
booktitle = {ICRA 2023},
year = {2023}
}