Stress-Testing Grounding Models on Synthetic Data

KubriCount is a synthetic benchmark designed to test foundation models in robotics by reframing counting tasks as verifiable prompt-following problems, emphasizing the importance of grounding instructions to avoid errors like a robot grabbing the wrong block. The article outlines a continuous workflow using the FiftyOne tool, which involves curating, annotating, generating, and evaluating synthetic data to identify and rectify model failures before deployment. KubriCount's dataset includes 110,507 synthetic images with over 7.3 million annotated objects, categorized into five explicit counting granularity levels, each with a target and a distractor set, to rigorously evaluate models' capabilities in distinguishing targets under specific prompts. The study highlights the often-overlooked biases and potential failures in foundation models, as illustrated by the NVIDIA LocateAnything-3B model's performance, which struggled with the hardest disambiguation tasks, often defaulting to visually dominant groups rather than the specified target. By using KubriCount, researchers can visualize generalization gaps and specific failure modes, informing future data generation and model training efforts to enhance the reliability and accuracy of open-world robotics applications.

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