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Related Experiment Video

Updated: Aug 12, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Leveraging explainability for understanding object descriptions in ambiguous 3D environments.

Fethiye Irmak Doğan¹, Gaspar I Melsión¹, Iolanda Leite¹

¹Division of Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.

Frontiers in Robotics and AI

|January 27, 2023

Summary

This summary is machine-generated.

Robots can now better understand user requests in 3D space by using explainability to identify objects, even ambiguous ones, without prior category limits. This method improves human-robot collaboration by considering depth information for clearer object recognition.

Keywords:

depth explainability real-world environments referring expression comprehension (REC)resolving ambiguities

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Area of Science:

Robotics
Computer Vision
Human-Robot Interaction

Background:

Effective human-robot collaboration requires robots to understand user requests in 3D space and resolve ambiguities.
Existing methods often limit object categories and neglect depth information, hindering real-world applications.
Understanding described objects in complex, unconstrained environments is a significant challenge.

Purpose of the Study:

To develop a novel method for robots to understand user-described objects in 3D scenes, overcoming limitations of existing approaches.
To improve ambiguity resolution in human-robot interaction by incorporating explainability and depth perception.
To enable robots to identify objects without pre-defined categories or constraints on natural language instructions.

Main Methods:

A novel method leveraging explainability to focus on active areas of an RGB scene for object identification.
Integration of depth dimension perception to enhance object identification and disambiguation.
Evaluation on diverse real-world images with ambiguous and non-ambiguous objects.

Main Results:

The proposed method successfully identifies described objects by focusing on salient regions, aiding ambiguity resolution.
Performance is superior to state-of-the-art baselines, particularly in scenes with objects undetectable by conventional detectors.
Incorporating depth features significantly boosts performance in disambiguating objects where depth is critical.

Conclusions:

Explainability-driven object identification, enhanced with depth information, is effective for resolving ambiguities in human-robot interaction.
The method broadens the scope of human-robot collaboration by handling unconstrained object categories and complex 3D environments.
This approach represents a significant advancement in enabling robots to better perceive and understand user requests in real-world scenarios.