Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines

TL;DR

This survey emphasizes the importance of data infrastructure, including datasets, benchmarks, and data engines, in advancing Vision-Language-Action models for robotics, highlighting current limitations and future challenges.

Contribution

It provides a systematic, data-centric analysis of VLA research, categorizing datasets, benchmarks, and data engines, and identifies key open challenges for future progress.

Findings

Identifies a fidelity-cost trade-off in datasets affecting large-scale collection.

Exposes gaps in current benchmarks for compositional generalization and reasoning.

Highlights limitations of current data engines in physical grounding and sim-to-real transfer.

Abstract

Despite remarkable progress in Vision--Language--Action (VLA) models, a central bottleneck remains underexamined: the data infrastructure that underlies embodied learning. In this survey, we argue that future advances in VLA will depend less on model architecture and more on the co-design of high-fidelity data engines and structured evaluation protocols. To this end, we present a systematic, data-centric analysis of VLA research organized around three pillars: datasets, benchmarks, and data engines. For datasets, we categorize real-world and synthetic corpora along embodiment diversity, modality composition, and action space formulation, revealing a persistent fidelity-cost trade-off that fundamentally constrains large-scale collection. For benchmarks, we analyze task complexity and environment structure jointly, exposing structural gaps in compositional generalization and long-horizon reasoning evaluation that existing protocols fail to address. For data engines, we examine simulation-based, video-reconstruction, and automated task-generation paradigms, identifying their shared limitations in physical grounding and sim-to-real transfer. Synthesizing these analyses, we distill four open challenges: representation alignment, multimodal supervision, reasoning assessment, and scalable data generation. Addressing them, we argue, requires treating data infrastructure as a first-class research problem rather than a background concern.