Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems
Abstract
We describe the winning entry to the Amazon Picking Challenge 2015. From the experience of building this system and competing, we derive several conclusions: (1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions - modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback. (2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. (3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra. This is an abridged version of a conference publication.
Cite
Text
Eppner et al. "Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/676Markdown
[Eppner et al. "Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/eppner2017ijcai-lessons/) doi:10.24963/IJCAI.2017/676BibTeX
@inproceedings{eppner2017ijcai-lessons,
title = {{Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems}},
author = {Eppner, Clemens and Höfer, Sebastian and Jonschkowski, Rico and Martín-Martín, Roberto and Sieverling, Arne and Wall, Vincent and Brock, Oliver},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4831-4835},
doi = {10.24963/IJCAI.2017/676},
url = {https://mlanthology.org/ijcai/2017/eppner2017ijcai-lessons/}
}