Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
Alex X. Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki
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.
BibTeX
@inproceedings{
lee2021beyond,
title={Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes},
author={Alex X. Lee and Coline Manon Devin and Yuxiang Zhou and Thomas Lampe and Konstantinos Bousmalis and Jost Tobias Springenberg and Arunkumar Byravan and Abbas Abdolmaleki and Nimrod Gileadi and David Khosid and Claudio Fantacci and Jose Enrique Chen and Akhil Raju and Rae Jeong and Michael Neunert and Antoine Laurens and Stefano Saliceti and Federico Casarini and Martin Riedmiller and raia hadsell and Francesco Nori},
booktitle={5th Annual Conference on Robot Learning },
year={2021},
url={https://openreview.net/forum?id=U0Q8CrtBJxJ}
}