Survey: Automation in Embodied AI Benchmark Construction Shifts Costs to Validation and Governance
A comprehensive arXiv survey examines how automation is transforming the construction of benchmarks for embodied AI systems across navigation, robotics, and autonomous driving. The research identifies a five-stage pipeline from task specification through evaluation, analyzing the transition from manual curation to AI-assisted and agentic workflows. The findings suggest that while automation reduces some costs, it redistributes burden toward validation, auditability, and long-term governance—critical for reliable AI evaluation.
The survey reviews embodied intelligence benchmarking across six major domains: navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control. It structures benchmark construction as a five-stage pipeline: requirement and task construction, data acquisition, data cleaning and annotation, benchmark suite generation with metrics, and evaluation execution with diagnostic feedback. For each stage, the authors analyze the evolution from purely manual curation through traditional automation, foundation-model assistance, and agentic closed-loop systems. A key finding is that automation does not simply reduce overall costs; instead, it reallocates expenses toward validation, auditability, version control, and maintenance. The survey compares qualitative costs across human labor, data and asset acquisition, compute and simulation, validation and debugging, governance, and rework risk. The authors conclude that progress in embodied AI evaluation depends not only on scaling benchmark suites but also on developing construction pipelines that are diagnosable, auditable, and responsibly refreshable.
What different sources said
- arXiv cs.AICenter
Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends
- arXiv cs.AICenter
Embodied-BenchClaw: An Autonomous Multi-Agent System for Embodied Spatial Intelligence Benchmark Construction
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