Imitating Task and Motion Planning with Visuomotor Transformers
Abstract
Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contrast, Task and Motion Planning (TAMP) can autonomously generate large-scale datasets of diverse demonstrations. In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation. To that end, we present a novel imitation learning system called OPTIMUS that trains large-scale visuomotor Transformer policies by imitating a TAMP agent. OPTIMUS introduces a pipeline for generating TAMP data that is specifically curated for imitation learning and can be used to train performant transformer-based policies. In this paper, we present a thorough study of the design decisions required to imitate TAMP and demonstrate that OPTIMUS can solve a wide variety of challenging vision-based manipulation tasks with over 70 different objects, ranging from long-horizon pick-and-place tasks, to shelf and articulated object manipulation, achieving 70 to 80% success rates. Video results and code at https://mihdalal.github.io/optimus/
Cite
@article{arxiv.2305.16309,
title = {Imitating Task and Motion Planning with Visuomotor Transformers},
author = {Murtaza Dalal and Ajay Mandlekar and Caelan Garrett and Ankur Handa and Ruslan Salakhutdinov and Dieter Fox},
journal= {arXiv preprint arXiv:2305.16309},
year = {2023}
}
Comments
Conference on Robot Learning (CoRL) 2023. 8 pages, 5 figures, 2 tables; 11 pages appendix (10 additional figures)