An End-to-End System for Accomplishing Tasks with Modular Robots: Perspectives for the AI Community
Abstract
The advantage of modular robot systems lies in their flexibility, but this advantage can only be realized if there exists some reliable, effective way of generating configurations (shapes) and behaviors (controlling programs) appropriate for a given task. In this paper, we present an end-to-end system for addressing tasks with modular robots, and demonstrate that it is capable of accomplishing challenging multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) Modular robot hardware. This paper condenses the material originally presented in Jing et al. 2016 into a shorter format suitable for a broad audience.
Cite
Text
Jing et al. "An End-to-End System for Accomplishing Tasks with Modular Robots: Perspectives for the AI Community." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/686Markdown
[Jing et al. "An End-to-End System for Accomplishing Tasks with Modular Robots: Perspectives for the AI Community." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/jing2017ijcai-end/) doi:10.24963/IJCAI.2017/686BibTeX
@inproceedings{jing2017ijcai-end,
title = {{An End-to-End System for Accomplishing Tasks with Modular Robots: Perspectives for the AI Community}},
author = {Jing, Gangyuan and Tosun, Tarik and Yim, Mark and Kress-Gazit, Hadas},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4879-4883},
doi = {10.24963/IJCAI.2017/686},
url = {https://mlanthology.org/ijcai/2017/jing2017ijcai-end/}
}