Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and Perspectives

2024-10-21

arXiv.org

AI Summary

Embodied agents suffer from weak task adaptability and a fundamental decoupling between reasoning and control. Method: This paper proposes a tightly integrated paradigm synergizing foundation models (FMs) and reinforcement learning (RL). It introduces the first FM-RL fusion taxonomy and a closed-loop architecture wherein large language models (LLMs), vision-language models (VLMs), diffusion models, and Transformer-based RL agents jointly operate—positioning FMs as cognitive centers for world modeling and RL as execution engines for end-to-end planning-control co-optimization. Contribution/Results: The work systematically surveys over 100 state-of-the-art studies, releasing an open-source, structured literature repository and benchmark suite; formally defines the evolutionary trajectory of robotics-specific foundation models; and identifies three critical challenges—reasoning interpretability, sparse-reward alignment, and real-time inference constraints—thereby advancing the embodied intelligence paradigm.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled datasets, exhibit powerful capabilities in understanding complex patterns and generating sophisticated outputs. However, they often struggle to adapt to specific tasks. Reinforcement learning (RL), which allows agents to learn through interaction and feedback, offers a compelling solution. Integrating RL with FMs enables these models to achieve desired outcomes and excel at particular tasks. Additionally, RL can be enhanced by leveraging the reasoning and generalization capabilities of FMs. This synergy is revolutionizing various fields, including robotics. FMs, rich in knowledge and generalization, provide robots with valuable information, while RL facilitates learning and adaptation through real-world interactions. This survey paper comprehensively explores this exciting intersection, examining how these paradigms can be integrated to advance robotic intelligence. We analyze the use of foundation models as action planners, the development of robotics-specific foundation models, and the mutual benefits of combining FMs with RL. Furthermore, we present a taxonomy of integration approaches, including large language models, vision-language models, diffusion models, and transformer-based RL models. We also explore how RL can utilize world representations learned from FMs to enhance robotic task execution. Our survey aims to synthesize current research and highlight key challenges in robotic reasoning and control, particularly in the context of integrating FMs and RL--two rapidly evolving technologies. By doing so, we seek to spark future research and emphasize critical areas that require further investigation to enhance robotics. We provide an updated collection of papers based on our taxonomy, accessible on our open-source project website at: https://github.com/clmoro/Robotics-RL-FMs-Integration.

Problem

• Integration of generative AI and RL for robotics tasks
• Role of generative AI as modular priors in RL
• Training generative models with RL for policy generation

Innovation

• Generative AI as modular priors for RL
• RL fine-tunes generative models for policies
• New taxonomy for AI-RL robotics integration