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Chain-of-Detection: Enhancing Cross-Granularity Robotic Perception for Object Manipulation.

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    Summary
    This summary is machine-generated.

    The chain-of-detection (CoD) framework enhances robotic perception by improving cross-granularity object detection. Combining CoD with Monte Carlo tree search (MCTS) automates dataset generation, boosting fine-grained detection and robotic manipulation success rates.

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    Area of Science:

    • Robotics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Cross-granularity object detection is crucial for robotic perception, enabling target identification at various detail levels.
    • Traditional methods struggle with the coarse-to-fine detection gap, hindering part association (e.g., cup and handle).
    • Vision-language models (VLMs) face fine-grained detection challenges due to limited annotated datasets.

    Purpose of the Study:

    • To develop a framework for step-by-step detection from coarse recognition to fine-grained localization.
    • To address limitations in fine-grained component recognition within existing detectors.
    • To automate the generation of fine-grained datasets for improved detector performance.

    Main Methods:

    • Proposed the chain-of-detection (CoD) framework for guided, step-by-step detection.
    • Integrated CoD with Monte Carlo tree search (MCTS) for automated fine-grained dataset generation.
    • Eliminated manual labeling requirements through MCTS-driven data synthesis.

    Main Results:

    • Achieved an average 17.31% improvement in robotic manipulation success rates for common objects.
    • Demonstrated a 51.39% improvement for larger object operations.
    • Reported approximately 50% improvement in simulated environments.

    Conclusions:

    • The CoD framework effectively advances cross-granularity detection capabilities.
    • Automated dataset generation via MCTS significantly enhances fine-grained detection performance.
    • The approach leads to substantial improvements in precise robotic manipulation tasks.