Minimal Mobile Systems via Cloud-based Adaptive Task Processing

The high cost of robots today has hindered their widespread use. Specifically, a limiting factor involves extensive hardware and software computational resources required to run various real-time robot functions, from intensive inference with large neural network models to costly storage and compute (e.g., GPUs). How can cloud-enabled mechanisms efficiently bring about low-cost but highly-functional robots today?

In this project, our goal is to develop an efficient distributed computing platform between a robot and the cloud.  We will develop an adaptive robot-cloud task management system that can intelligently off-load real-time computation to the cloud while enabling highly affordable and efficient on-board operation. We will also work to integrate various cloud-enabled functionalities with existing open-source tools for robotics development.

Figure caption: Cloud-based off-loading mechanisms can enable extremely low-cost robots to perform complex high-capacity tasks.

This project is supported by the Red Hat Collaboratory at Boston University.


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