GRID: A Platform for General Robot Intelligence Development
This addresses the problem of costly and application-specific robot intelligence development for robotics researchers and engineers, though it appears incremental as a platform built on existing foundation models.
The authors tackled the expensive and hard-to-generalize process of developing machine intelligence for robots by introducing GRID, a platform that enables robots to learn, compose, and adapt skills, which dramatically accelerates development in aerial robotics scenarios.
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.