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

2024-04-28

arXiv.org

AI Summary

Deploying general-purpose robots in unstructured environments to perform diverse manipulation tasks remains challenging due to the need for robust perception, adaptive planning, and precise control under open-world uncertainty. Method: This paper proposes the first hierarchical manipulation framework integrating multimodal foundation models (vision, language, and embodied AI), systematically adapting them across perception, planning, and control modules within a closed-loop, autonomous-driving-inspired architecture. It unifies ROS/Isaac Gym robotics platforms with prompt engineering and embodied reasoning techniques to define functional roles and inter-module coordination mechanisms. Contribution/Results: The work establishes a comprehensive foundation-model integration paradigm spanning the full manipulation stack—providing a theoretical blueprint, concrete implementation roadmap, and standardized evaluation benchmarks. It rigorously identifies critical failure modes and open research questions. Widely cited in the field, this framework has enabled and informed multiple subsequent empirical studies in embodied AI and robotic manipulation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Enhancing robot manipulation in unstructured environments using foundation models
• Developing a framework for general manipulation capability with foundation models
• Addressing challenges and risks in integrating foundation models into robot learning

Innovation

• Embedding foundation models into manipulation tasks
• Proposing comprehensive framework for general manipulation
• Addressing challenges with multiple functional modules