Neural Scaling Laws in Robotics

TL;DR

This study systematically analyzes neural scaling laws in robotics, revealing that larger models and more data significantly enhance robotic task performance and enable new capabilities, following power-law relationships.

Contribution

First comprehensive analysis of neural scaling laws in robotics, quantifying how data, model size, and compute influence robotic task performance.

Findings

Performance improves with increased resources following power-law.

Robotic models scale faster than language models.

Scaling leads to emergence of new robot capabilities.

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.