Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation
Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute grounding, high-fidelity spatial perception, and constraint-respecting motor execution. However, current embodied AI benchmarks collapse these capacities into binary success rates, systematically inflating reported capabilities by up to 70% and masking the architectural bottlenecks that impede real-world deployment. We introduce MetaFine, a diagnostic meta-evaluation framework that disentangles manipulation competency along three axes: understanding, perception, and controlled behavior. Built on a compositional task graph, MetaFine absorbs het...
| Authors | He-Yang Xu, Pengyuan Zhang, Zongyuan Ge et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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