Abstract

Building general-purpose robots that can operate seamlessly, in any environment, with any objects, and utilizing various skills to complete diverse tasks has been a long-standing goal in Artificial Intelligence. Unfortunately, however, most existing robotic systems have been constrained—having been trained on specific datasets, for specific tasks, and within specific envi- ronments. These systems usually require extensively-labeled data, rely on task-specific models, have numerous generalization issues when deployed in real-world scenarios, and struggle to remain robust to distribution shifts. Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i.e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of Robotics, and also exploring (ii) what a robotics-specific foundation model (henceforth, robotics foundation model) would look like. We begin by providing an overview of what constitutes a conventional robotic system and the fundamental barriers to making it universally applicable. Next, we establish a taxonomy to discuss current work exploring ways to leverage existing foundation models for Robotics and develop new ones catered to Robotics. Finally, we discuss key challenges and promising future directions in using foundation models for enabling universally-applicable robotic systems

Published at: In Submission.