Integration of Large Language Models in Cognitive Architectures for Autonomous Robots

แนวคิดหลัก

Large Language Models (LLMs) are proposed to enhance reasoning capabilities in cognitive architectures for autonomous robots, despite facing performance challenges compared to traditional symbolic planning systems.

บทคัดย่อ

The content discusses the integration of Large Language Models (LLMs) within cognitive architectures for autonomous robots. It proposes using LLMs to improve reasoning capabilities and planning processes. The paper details the design, development, and deployment of LLMs within the ROS 2-integrated cognitive architecture MERLIN2. The integration aims to transition from a PDDL-based planner system to a natural language planning system. Results show that while classical approaches outperform LLMs in performance, the proposed solution enhances interaction through natural language. The paper also evaluates the impact of incorporating LLMs quantitatively and qualitatively.
Directory:

Abstract

Proposes using Large Language Models (LLMs) in cognitive architectures.

Introduction

Discusses symbolic reasoning systems and challenges with predefined rules.

Benefits of LLMs in Robotics

Enables natural language interaction, knowledge retrieval, explainability.

Achievements of LLMs in Reasoning Tasks

Highlights zero-shot reasoning capabilities and prompting techniques.

Behavior Generation Paradigms in Autonomous Robots

Discusses deliberative architectures, subsumption architectures, reactive architectures, and hybrid architectures.

Proposal Focus on Reasoning

Integrates LLMs into existing cognitive architecture for enhanced reasoning.

Evaluation Methodology

Conducted human-robot interaction experiments to evaluate performance.

Results Comparison

Compares execution time and traveled distance between MERLIN2 and different versions with LLM integration.

Discussion on Performance

Analyzes results showing superior performance of MERLIN2 over LLM alternatives.

Challenges with LLM Integration

Identifies increased complexity and feedback issues with incorporating LLMs.

สถิติ

"Large Language Models (LLMs) have emerged as tools to process natural language for different tasks."
"Results show that a classical approach achieves better performance but the proposed solution provides an enhanced interaction through natural language."
"The robot must be able to navigate through the apartment and speak to people."

คำพูด

"The proposal focuses on reasoning by integrating Large Language Models (LLMs) into existing cognitive architecture."
"Results demonstrate that while classical approaches outperform LLMs in performance, the proposed solution enhances interaction through natural language."

ข้อมูลเชิงลึกที่สำคัญจาก

Integration of Large Language Models within Cognitive Architectures for Autonomous Robots

by Migu... ที่ arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.14945.pdf

สอบถามเพิ่มเติม

How can the limitations of using Large Language Models (LLMs) be addressed for improved performance?

To address the limitations of using Large Language Models (LLMs) for improved performance, several strategies can be implemented:

Quantization: Implementing quantization techniques to reduce the precision of model parameters and activations from floating-point numbers to fixed-point numbers can significantly decrease memory and computational requirements, making LLMs more feasible for deployment in resource-constrained environments like robots.

Optimized Prompt Engineering: Enhancing prompt engineering methods by utilizing specific embedding models and refining the Retrieval Augmented Generation (RAG) process can streamline knowledge retrieval and improve overall efficiency.

Graph Algorithms: Exploring graph algorithms to optimize world state representation instead of relying solely on RAG could potentially enhance performance by organizing knowledge more effectively within a cognitive architecture.

Hybrid Approaches: Combining LLMs with other symbolic techniques such as anthologies or incorporating smaller but more accurate language models may lead to better results in reasoning processes while mitigating some of the challenges faced with larger models.

Feedback Mechanisms: Leveraging feedback mechanisms from checking goal states, even though it might increase execution times, can provide valuable insights into planning stages and refine subsequent plan generations based on unmet goals.

What are potential implications of relying solely on symbolic planning systems without integrating LLMs?

Relying solely on symbolic planning systems without integrating Large Language Models (LLMs) could have several implications:

Limited Adaptability: Symbolic planning systems often struggle with adapting to complex or dynamic environments due to their reliance on predefined rules and structures. This limitation may hinder a robot's ability to handle unforeseen scenarios effectively.

Reduced Natural Language Interaction: Without LLM integration, robots may lack natural language processing capabilities that enable seamless communication with humans through text-based interactions, limiting user-friendliness and accessibility.

Knowledge Retrieval Challenges: Symbolic planning systems typically require explicit domain definitions which might not cover all possible scenarios comprehensively. This could result in difficulties accessing diverse information sources compared to leveraging LLMs' vast knowledge repositories.

Less Efficient Reasoning Processes: Purely symbolic approaches may face challenges in handling nuanced reasoning tasks efficiently compared to large language models that excel at tasks involving multi-step reasoning or zero-shot inference.

How might advancements in Visual Language Models (VLM) impact human-robot interactions beyond text-based communication?

Advancements in Visual Language Models (VLM) have the potential to revolutionize human-robot interactions beyond text-based communication by introducing new dimensions such as visual understanding and interpretation:

Enhanced Perception Capabilities: VLMs can enable robots to interpret images captured through cameras, allowing them to understand visual cues from their environment better.

Improved Object Recognition: By integrating VLMs into robotic systems, object recognition tasks can be enhanced through image analysis combined with natural language processing capabilities.

Gesture Recognition: VLMs could facilitate gesture recognition features where robots interpret human gestures alongside verbal commands for more intuitive interaction.

4..Multimodal Communication: With VML technology enabling both visual understanding and linguistic comprehension simultaneously, robots will be ableto engagein richer multimodal conversations combining speech,text,and visuals.
5..Explainability Through Visualization: Robots equippedwith VLMScan generate explanations visually,supporting users’understandingof complex conceptsor actions performedbythe robot
6..**Autonomous Navigation Assistance:Visual input processedbyVLMScan aidrobotsin navigatingcomplexenvironmentsmoreeffectively,reducingrelianceonpredefinedmapsor navigationalmarkers