Embodied-BenchClaw: An Autonomous Multi-Agent System for Embodied Spatial Intelligence Benchmark Construction

Problem
Existing benchmarks for embodied spatial intelligence are static, labor-intensive to create, and often fail to adapt as models evolve, leading to saturation in distinguishing new capabilities. This paper addresses the gap in automated benchmark construction, which is crucial for evaluating advancements in embodied AI. The authors highlight that current benchmarks lack reusability and maintainability, which hampers the assessment of emerging models. This work is presented as a preprint, indicating it has not yet undergone peer review.

Method
The authors propose Embodied-BenchClaw, an autonomous multi-agent system that constructs benchmarks through a five-stage pipeline: intent blueprinting, data collection, structuring and cleaning, benchmark synthesis, and evaluation reporting. The system is coordinated by three specialized agents responsible for planning, construction, and evaluation. A key innovation is the introduction of an extensible Skill Library, which enhances the composability and reliability of the benchmarks. The authors also implement process quality control to ensure the benchmarks are verifiable and repairable. The system is designed to cover a wide range of tasks, including indoor and outdoor spatial reasoning, robotic manipulation, quadruped navigation, UAV understanding, and static benchmark enhancement.

Results
Embodied-BenchClaw was evaluated through various methodologies, including human evaluations, judge-based assessments, consistency checks, and cost analyses. The results indicate that the system can produce benchmarks that are verifiable, executable, maintainable, and diagnostically useful, significantly reducing the manual effort required for benchmark construction. While specific numerical results are not detailed in the abstract, the authors claim that their benchmarks outperform existing static benchmarks in terms of adaptability and utility.

Limitations
The authors acknowledge that while Embodied-BenchClaw automates benchmark construction, the quality of the benchmarks is still dependent on the initial user-specified evaluation intent. Additionally, the system’s performance may vary based on the complexity of the tasks and the diversity of the data sources used. The paper does not address potential biases in the data collection process or the scalability of the system across different domains.

Why it matters
The development of Embodied-BenchClaw has significant implications for the field of embodied AI, as it provides a scalable and efficient method for creating benchmarks that can evolve alongside advancements in AI capabilities. This work facilitates more accurate evaluations of embodied spatial intelligence, which is critical for the development of robust AI systems. The ability to generate dynamic benchmarks can lead to more nuanced insights into model performance and capabilities, ultimately driving innovation in the field. This research is available on arXiv.