GRID: A Platform for General Robot Intelligence Development

TL;DR

The paper introduces GRID, a versatile platform that accelerates the development of general robot intelligence by enabling robots to learn, adapt, and compose skills across different hardware and environments using foundation models.

Contribution

GRID provides an extensible, modular platform that integrates foundation models for general robot intelligence, addressing scalability and adaptability challenges in robotics development.

Findings

Demonstrated in aerial robotics scenarios

Accelerates development of intelligent robots

Supports diverse hardware and software integration

Abstract

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.