Data Assessment for Embodied Intelligence

2025-11-12

AI Summary

In embodied intelligence, dataset evaluation faces two key bottlenecks: (1) existing methods fail to comprehensively characterize **diversity** of multimodal data—relying solely on task/scene statistics or unimodal analysis—and (2) there is no efficient, interpretable *a priori* assessment of **learnability**, as current approaches depend on costly *post hoc* model training. This paper proposes the first unified multimodal representation framework that jointly models information entropy and learns data-driven quantifications of learnability. Specifically, we introduce **diversity entropy**—a continuous, cross-modal metric for diversity—and design a training-free, interpretable evaluation module enabling efficient, explainable, pre-training quantification of dataset quality. Evaluated across multiple simulated and real-world embodied datasets, our method reliably identifies data deficiencies and provides actionable guidance for constructing high-quality embodied datasets.

Technology Category

Intelligent Robots: Embodied AIMachine Learning: Multimodal LearningComputer Vision: Multi-modal Vision

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In embodied intelligence, datasets play a pivotal role, serving as both a knowledge repository and a conduit for information transfer. The two most critical attributes of a dataset are the amount of information it provides and how easily this information can be learned by models. However, the multimodal nature of embodied data makes evaluating these properties particularly challenging. Prior work has largely focused on diversity, typically counting tasks and scenes or evaluating isolated modalities, which fails to provide a comprehensive picture of dataset diversity. On the other hand, the learnability of datasets has received little attention and is usually assessed post-hoc through model training, an expensive, time-consuming process that also lacks interpretability, offering little guidance on how to improve a dataset. In this work, we address both challenges by introducing two principled, data-driven tools. First, we construct a unified multimodal representation for each data sample and, based on it, propose diversity entropy, a continuous measure that characterizes the amount of information contained in a dataset. Second, we introduce the first interpretable, data-driven algorithm to efficiently quantify dataset learnability without training, enabling researchers to assess a dataset's learnability immediately upon its release. We validate our algorithm on both simulated and real-world embodied datasets, demonstrating that it yields faithful, actionable insights that enable researchers to jointly improve diversity and learnability. We hope this work provides a foundation for designing higher-quality datasets that advance the development of embodied intelligence.

Problem

• Evaluating dataset diversity in multimodal embodied intelligence data
• Assessing dataset learnability without expensive model training processes
• Lacking interpretable tools for comprehensive embodied dataset quality assessment

Innovation

• Multimodal unified representation for data samples
• Diversity entropy measures dataset information content
• Interpretable algorithm quantifies learnability without training