Title: Data Assessment for Embodied Intelligence
Primary area: applications to robotics, autonomy, planning
Keywords: Benchmark; Embodied AI; Data-centric AI;

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 metrics. First, we construct a unified multimodal representation for each data 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 multiple simulated and real-world embodied datasets, demonstrating that it yields faithful, actionable insights, enabling 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.

TL;DR: A Quality Evaluation Toolkit for Embodied Data