Neural Scaling Laws in Robotics

2024-05-22

AI Summary

The scaling laws governing robot foundation models remain poorly understood, hindering principled resource allocation and model development. Method: We conduct the first systematic meta-analysis of 327 papers, employing power-law fitting, cross-task performance normalization, and a comparative evaluation framework contrasting robot models with large language models (LLMs). Contribution/Results: (1) Robot task performance scales more steeply with resource growth than language tasks (mean scaling exponent β ≈ 0.52 vs. 0.35); (2) emergent capabilities—including multi-step manipulation generalization and cross-scenario zero-shot adaptation—arise with model expansion; (3) mid- to low-performing robot tasks exhibit heightened sensitivity to increases in data volume and compute budget. These findings establish the first quantitative scaling laws for robot foundation models, providing theoretically grounded guidance for scalable model design, data curation, and computational investment in robotics AI.

Technology Category

• Intelligent Robots: Other Foundations and Applications
• Machine Learning: Scalability of ML Systems
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Neural scaling laws have driven significant advancements in machine learning, particularly in domains like language modeling and computer vision. However, the exploration of neural scaling laws within robotics has remained relatively underexplored, despite the growing adoption of foundation models in this field. This paper represents the first comprehensive study to quantify neural scaling laws for Robot Foundation Models (RFMs) and Large Language Models (LLMs) in robotics tasks. Through a meta-analysis of 327 research papers, we investigate how data size, model size, and compute resources influence downstream performance across a diverse set of robotic tasks. Consistent with previous scaling law research, our results reveal that the performance of robotic models improves with increased resources, following a power-law relationship. Promisingly, the improvement in robotic task performance scales notably faster than language tasks. This suggests that, while performance on downstream robotic tasks today is often moderate-to-poor, increased data and compute are likely to signficantly improve performance in the future. Also consistent with previous scaling law research, we also observe the emergence of new robot capabilities as models scale.

Problem

• Neural Scaling Laws
• Robotics
• Performance Optimization

Innovation

• Neural Scaling Laws
• Robotics Performance
• Large-scale Models