A Compositional Paradigm for Foundation Models: Towards Smarter Robotic Agents

TL;DR

This paper proposes a compositional paradigm for foundation models, integrating continual learning principles to enhance adaptability and efficiency in robotic agents across diverse tasks and real-world scenarios.

Contribution

It introduces a novel framework combining compositionality and continual learning to improve foundation models' flexibility and real-world applicability in robotics.

Findings

Enhanced adaptability in robotic control tasks

Improved efficiency in model updating

Greater robustness in dynamic environments

Abstract

The birth of Foundation Models brought unprecedented results in a wide range of tasks, from language to vision, to robotic control. These models are able to process huge quantities of data, and can extract and develop rich representations, which can be employed across different domains and modalities. However, they still have issues in adapting to dynamic, real-world scenarios without retraining the entire model from scratch. In this work, we propose the application of Continual Learning and Compositionality principles to foster the development of more flexible, efficient and smart AI solutions.