AI algorithms in development with Aurora for DARPA ShELL effortNews
November 17, 2021
BAY AREA, Calif. Under a new Defense Advanced Research Projects Agency (DARPA) contract for the Shared-Experience Lifelong Learning (ShELL) Artificial Intelligence Exploration (AIE) effort, Aurora aims to develop AI algorithms to achieve life-long learning for agents that learn new tasks in changing environments while accounting for limitations in communications and hardware configuration.
In the Lifelong Learning (LL) framework, agents continually learn as they encounter new tasks or situations while in the field. The ShELL program extends this approach to many agents that share these continuous experiences among the whole population, aiming to improve and accelerate the training of each agent in the group, according to officials.
Additionally, the DARPA ShELL agents seek to address size, weight, and power (SWaP) and computing-constrained platforms with limited communications. The program will be executed in two phases, with a phase one feasibility study and phase two proof of concept.
Under a subaward of this program, Aurora claims it will collaborate with the Aerospace Controls Lab in MIT’s Department of Aeronautics and Astronautics on a novel AI agent learning methodology and optimize the agents to operate on low SWaP and communications constraints.