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  1. Home
  3. Embodied Spatial Affordance: Spatial-aware Affordance Learning For Embodied Navigation And Manipulation.
  1. Home
  3. Embodied Spatial Affordance: Spatial-aware Affordance Learning For Embodied Navigation And Manipulation.

Related Experiment Video

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Published on: January 26, 2024

Embodied Spatial Affordance: Spatial-Aware Affordance Learning for Embodied Navigation and Manipulation.

Xiaoshuai Hao, Yingbo Tang, Lingfeng Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 4, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    EspA, a new image-to-keypoint model, enhances embodied agents by precisely localizing object and free space affordances from 2D images. This spatial-aware learning model improves robotic navigation and manipulation tasks.

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    Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

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    Area of Science:

    • Robotics and Artificial Intelligence
    • Computer Vision
    • Human-Robot Interaction

    Background:

    • Embodied agents require understanding spatial context and object affordances for navigation and manipulation.
    • Current Vision-Language Models (VLMs) struggle with precise spatial understanding and affordance localization from images, limiting their application in robotics.
    • Bridging the gap between high-level reasoning and low-level actionable commands is crucial for embodied AI.

    Purpose of the Study:

    • To introduce EspA, a novel image-to-keypoint model for spatial-aware affordance learning.
    • To enable precise pixel-level localization of both object and free space affordances directly from 2D image inputs.
    • To improve the translation of language instructions into actionable 3D coordinates for embodied agents.

    Main Methods:

    • Developed a hierarchical vision-language architecture for joint reasoning of object and free space affordances.
    • Introduced the Embodied Spatial Affordance (ESA) dataset with fine-grained annotations for embodied interactions.
    • Implemented an image-to-keypoint approach to predict affordance keypoints and project them into 3D space using depth information.

    Main Results:

    • EspA demonstrates superior performance in predicting object and free space affordances compared to state-of-the-art VLMs.
    • The model shows enhanced capabilities in real-world embodied navigation and manipulation tasks.
    • Achieved significant improvements in image-based spatial reasoning for embodied agents.

    Conclusions:

    • EspA provides a scalable solution for translating high-level instructions into low-level actionable affordances for embodied agents.
    • The proposed method advances embodied AI by enabling more robust and versatile interaction with physical environments.
    • The publicly available dataset and code will foster future research in embodied spatial understanding.