  Scaling laws have driven remarkable progress across machine learning domains
like language modeling and computer vision. However, the exploration of scaling
laws in embodied AI and robotics has been limited, despite the rapidly
increasing usage of machine learning in this field. This paper presents the
first study to quantify scaling laws for Robot Foundation Models (RFMs) and the
use of LLMs in robotics tasks. Through a meta-analysis spanning 198 research
papers, we analyze how key factors like compute, model size, and training data
quantity impact model performance across various robotic tasks. Our findings
confirm that scaling laws apply to both RFMs and LLMs in robotics, with
performance consistently improving as resources increase. The power law
coefficients for RFMs closely match those of LLMs in robotics, resembling those
found in computer vision and outperforming those for LLMs in the language
domain. We also note that these coefficients vary with task complexity, with
familiar tasks scaling more efficiently than unfamiliar ones, emphasizing the
need for large and diverse datasets. Furthermore, we highlight the absence of
standardized benchmarks in embodied AI. Most studies indicate diminishing
returns, suggesting that significant resources are necessary to achieve high
performance, posing challenges due to data and computational limitations.
Finally, as models scale, we observe the emergence of new capabilities,
particularly related to data and model size.
