What Foundation Models can Bring for Robot Learning in Manipulation : A Survey

TL;DR

This survey explores how foundation models can enhance robot manipulation learning by providing a comprehensive framework, addressing challenges, and outlining future research directions for achieving general manipulation capabilities.

Contribution

It introduces a novel overarching framework integrating foundation models into robot manipulation and analyzes their roles across different modules.

Findings

Foundation models can significantly improve generalization in robot manipulation.

A comprehensive framework for integrating foundation models into manipulation tasks is proposed.

Current approaches face challenges like domain adaptation and safety risks.

Abstract

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle in achieving this goal lies in the robots' ability to manipulate objects in their unstructured surrounding environments according to different tasks. The learning-based approach is considered an effective way to address generalization. The impressive performance of foundation models in the fields of computer vision and natural language suggests the potential of embedding foundation models into manipulation tasks as a viable path toward achieving general manipulation capability. However, we believe achieving general manipulation capability requires an overarching framework akin to auto driving. This framework should encompass multiple functional modules, with different foundation models assuming distinct roles in facilitating general manipulation capability. This survey focuses on the contributions of foundation models to robot learning for manipulation. We propose a comprehensive framework and detail how foundation models can address challenges in each module of the framework. What's more, we examine current approaches, outline challenges, suggest future research directions, and identify potential risks associated with integrating foundation models into this domain.