RTML: Large: Co-design of Hardware and Algorithms for Energy-efficient Robot Learning

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

Miniature low-energy autonomous robotic vehicles, ranging from insect-size flyers to palm- size satellites, hold the potential for tremendous impact in a diverse set of industries, including consumer electronics, high-bandwidth communications, search and rescue operations, and space exploration, just to name a few. Next-generation low-energy computing hardware that will enable these applications must be adaptable, i.e., recognizing new environments on the fly, learning their characteristic features in real time, and adapting its computing strategy to minimize the energy consumption required for computing task. This project will help realize vehicles that are able to improve the accuracy of their perception and decision making algorithms, simply by experimenting with obtaining a diverse set of viewpoints of the environment and utilizing the knowledge of its motion to ground and improve its observation via machine learning. The project also seeks to develop new graduate and undergraduate courses at MIT, it will enable outreach for high school students, involve women and underrepresented groups, thus helping train the future US workforce. This project will develop real-time robot learning algorithms and hardware focusing on three core areas. Firstly, the project will develop real-time continuous robot learning systems that improve performance of robot perception and decision making by rapid learning in new environments. Secondly, the project will develop real-time active robot learning systems to efficiently decide the balance between improving accuracy of perception and decision making algorithms and focusing on accomplishing the task at hand. Thirdly, the project will develop real-time adaptable robot learning systems for energy scalable perception and decision making, where the design allows for efficient accuracy-energy tradeoffs. The project will help develop new hardware and algorithms for real-time robot learning, by enabling new low-energy robotic systems. The project will also collaborate with a synergistic DARPA program for related hardware development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.