Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
Abstract
We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple “pick-and-place” solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer. Our learned policies can efficiently handle multiple object combinations in the real world and exhibit a large variety of stacking skills. In a large experimental study, we investigate what choices matter for learning such general vision-based agents in simulation, and what affects optimal transfer to the real robot. We then leverage data collected by such policies and improve upon them with offline RL. A video and a blog post of our work are provided as supplementary material.
Cite
Text
Lee et al. "Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes." Conference on Robot Learning, 2021.Markdown
[Lee et al. "Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes." Conference on Robot Learning, 2021.](https://mlanthology.org/corl/2021/lee2021corl-beyond/)BibTeX
@inproceedings{lee2021corl-beyond,
title = {{Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes}},
author = {Lee, Alex X. and Devin, Coline Manon and Zhou, Yuxiang and Lampe, Thomas and Bousmalis, Konstantinos and Springenberg, Jost Tobias and Byravan, Arunkumar and Abdolmaleki, Abbas and Gileadi, Nimrod and Khosid, David and Fantacci, Claudio and Chen, Jose Enrique and Raju, Akhil and Jeong, Rae and Neunert, Michael and Laurens, Antoine and Saliceti, Stefano and Casarini, Federico and Riedmiller, Martin and Hadsell, Raia and Nori, Francesco},
booktitle = {Conference on Robot Learning},
year = {2021},
pages = {1089-1131},
volume = {164},
url = {https://mlanthology.org/corl/2021/lee2021corl-beyond/}
}