  Developing machine intelligence abilities in robots and autonomous systems is
an expensive and time consuming process. Existing solutions are tailored to
specific applications and are harder to generalize. Furthermore, scarcity of
training data adds a layer of complexity in deploying deep machine learning
models. We present a new platform for General Robot Intelligence Development
(GRID) to address both of these issues. The platform enables robots to learn,
compose and adapt skills to their physical capabilities, environmental
constraints and goals. The platform addresses AI problems in robotics via
foundation models that know the physical world. GRID is designed from the
ground up to be extensible to accommodate new types of robots, vehicles,
hardware platforms and software protocols. In addition, the modular design
enables various deep ML components and existing foundation models to be easily
usable in a wider variety of robot-centric problems. We demonstrate the
platform in various aerial robotics scenarios and demonstrate how the platform
dramatically accelerates development of machine intelligent robots.
