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Related Experiment Video

Updated: Jul 10, 2026

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Knowledge enhanced bottom-up affordance grounding for robotic interaction.

Wen Qu¹, Xiao Li¹, Xiao Jin¹

¹Computer Science and Technology, Dalian Martime University, Dalian, Liaoning, China.

Peerj. Computer Science

|July 10, 2024

Summary

This summary is machine-generated.

This study introduces KBAG-Net, a novel approach for robots to understand natural language instructions using external knowledge, improving object manipulation accuracy. A new semi-automatic data generation method also speeds up dataset creation for human-robot communication.

Keywords:

Affordance grounding Affordance segmentation Dataset construction Knowledge engineering Multimodal learning Robotic interaction

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Area of Science:

Robotics and Human-Computer Interaction
Artificial Intelligence
Computer Vision

Background:

Supervised multimodal deep learning is prevalent for language-conditioned robot manipulation.
Existing methods struggle with limited external knowledge and require extensive manually annotated datasets.
This necessitates advancements in natural language understanding for robots.

Purpose of the Study:

To enhance natural language understanding in robots by incorporating external knowledge.
To improve the accuracy of object grasping affordance segmentation.
To develop a semi-automatic method for generating language-guided manipulation datasets.

Main Methods:

Proposed the knowledge enhanced bottom-up affordance grounding network (KBAG-Net).
Integrated external knowledge to improve natural language comprehension.
Introduced a semi-automatic data generation technique for dataset creation.

Main Results:

KBAG-Net demonstrated superior performance compared to existing methods.
Achieved significant improvements in mean Intersection over Union (mIoU) by 12.98% and 1.22% on two datasets.
The semi-automatic method facilitates rapid dataset establishment.

Conclusions:

Incorporating external knowledge significantly enhances robot's natural language understanding and manipulation capabilities.
The proposed KBAG-Net offers a more robust and efficient solution for language-guided robot tasks.
The publicly available data generation method supports community engagement and further research.