RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots

Qinjie Lin, Guo Ye, Jiayi Wang, Han Liu
Proceedings of the 5th Conference on Robot Learning, PMLR 164:1789-1794, 2022.

Abstract

We propose RoboFlow, a cloud-based workflow management system orchestrating the pipelines of developing AI-enhanced robots. Unlike most traditional robotic development processes that are essentially process-centric, RoboFlow is data-centric. This striking property makes it especially suitable for developing AI-enhanced robots in which data play a central role. More specifically, RoboFlow models the whole robotic development process into 4 building modules (1. data processing, 2. algorithmic development, 3. back testing and 4. application adaptation) interacting with a centralized data engine. All these building modules are containerized and orchestrated under a unified interfacing framework. Such an architectural design greatly increases the maintainability and re-usability of all the building modules and enables us to develop them in a fully parallel fashion. To demonstrate the efficacy of the developed system, we exploit it to develop two prototype systems named “Egomobility" and “Egoplan". Egomobility provides general-purpose navigation functionalities for a wide variety of mobile robots and Egoplan solves path planning problems in high dimensional continuous state and action spaces for robot arms. Our result shows that RoboFlow can significantly streamline the whole development lifecycle and the same workflow is applicable to numerous intelligent robotic applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v164-lin22c, title = {RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots}, author = {Lin, Qinjie and Ye, Guo and Wang, Jiayi and Liu, Han}, booktitle = {Proceedings of the 5th Conference on Robot Learning}, pages = {1789--1794}, year = {2022}, editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard}, volume = {164}, series = {Proceedings of Machine Learning Research}, month = {08--11 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v164/lin22c/lin22c.pdf}, url = {https://proceedings.mlr.press/v164/lin22c.html}, abstract = {We propose RoboFlow, a cloud-based workflow management system orchestrating the pipelines of developing AI-enhanced robots. Unlike most traditional robotic development processes that are essentially process-centric, RoboFlow is data-centric. This striking property makes it especially suitable for developing AI-enhanced robots in which data play a central role. More specifically, RoboFlow models the whole robotic development process into 4 building modules (1. data processing, 2. algorithmic development, 3. back testing and 4. application adaptation) interacting with a centralized data engine. All these building modules are containerized and orchestrated under a unified interfacing framework. Such an architectural design greatly increases the maintainability and re-usability of all the building modules and enables us to develop them in a fully parallel fashion. To demonstrate the efficacy of the developed system, we exploit it to develop two prototype systems named “Egomobility" and “Egoplan". Egomobility provides general-purpose navigation functionalities for a wide variety of mobile robots and Egoplan solves path planning problems in high dimensional continuous state and action spaces for robot arms. Our result shows that RoboFlow can significantly streamline the whole development lifecycle and the same workflow is applicable to numerous intelligent robotic applications.} }
Endnote
%0 Conference Paper %T RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots %A Qinjie Lin %A Guo Ye %A Jiayi Wang %A Han Liu %B Proceedings of the 5th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2022 %E Aleksandra Faust %E David Hsu %E Gerhard Neumann %F pmlr-v164-lin22c %I PMLR %P 1789--1794 %U https://proceedings.mlr.press/v164/lin22c.html %V 164 %X We propose RoboFlow, a cloud-based workflow management system orchestrating the pipelines of developing AI-enhanced robots. Unlike most traditional robotic development processes that are essentially process-centric, RoboFlow is data-centric. This striking property makes it especially suitable for developing AI-enhanced robots in which data play a central role. More specifically, RoboFlow models the whole robotic development process into 4 building modules (1. data processing, 2. algorithmic development, 3. back testing and 4. application adaptation) interacting with a centralized data engine. All these building modules are containerized and orchestrated under a unified interfacing framework. Such an architectural design greatly increases the maintainability and re-usability of all the building modules and enables us to develop them in a fully parallel fashion. To demonstrate the efficacy of the developed system, we exploit it to develop two prototype systems named “Egomobility" and “Egoplan". Egomobility provides general-purpose navigation functionalities for a wide variety of mobile robots and Egoplan solves path planning problems in high dimensional continuous state and action spaces for robot arms. Our result shows that RoboFlow can significantly streamline the whole development lifecycle and the same workflow is applicable to numerous intelligent robotic applications.
APA
Lin, Q., Ye, G., Wang, J. & Liu, H.. (2022). RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots. Proceedings of the 5th Conference on Robot Learning, in Proceedings of Machine Learning Research 164:1789-1794 Available from https://proceedings.mlr.press/v164/lin22c.html.

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