Are Transformers Truly Foundational for Robotics?

2024-11-25

arXiv.org

AI Summary

This paper challenges the applicability of GPT-style large language models (LLMs) in autonomous robotics, highlighting fundamental bottlenecks—including excessive computational demand, prolonged training cycles, reliance on off-board infrastructure, and severe difficulties in embedded deployment. Method: The authors conduct the first systematic cross-disciplinary comparison between Transformer architectures and insect nervous systems, establishing an evaluation framework grounded in computational neuroscience, robotics, and AI architecture design. Core metrics include energy efficiency, real-time responsiveness, and embedded feasibility. Contribution/Results: The study distills biologically inspired design principles tailored for resource-constrained robots, directly contesting the “larger models imply greater generality” paradigm. It provides a theoretical foundation and practical roadmap for developing sample-efficient, low-latency, fully embedded embodied intelligence—advancing toward compact, neuro-inspired AI systems deployable on edge robotic platforms.

Technology Category

• Intelligent Robots: Embodied AI
• Natural Language Processing: Safety and Robustness
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Generative Pre-Trained Transformers (GPTs) are hyped to revolutionize robotics. Here we question their utility. GPTs for autonomous robotics demand enormous and costly compute, excessive training times and (often) offboard wireless control. We contrast GPT state of the art with how tiny insect brains have achieved robust autonomy with none of these constraints. We highlight lessons that can be learned from biology to enhance the utility of GPTs in robotics.

Problem

• Evaluate GPTs in robotics
• Compare GPTs with insect brains
• Explore biological lessons for robotics

Innovation

• Transformers in robotics
• Contrast with insect brains
• Biology-inspired GPT enhancement