What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots

TL;DR

This paper presents a deep learning approach for functional scene understanding in indoor environments, enabling robots to recognize and localize functional areas crucial for interaction, validated on a new dataset and generalizable across scenes.

Contribution

The paper introduces a novel two-stage deep detection pipeline for functional scene understanding and creates a new dataset for evaluation, improving robot scene comprehension capabilities.

Findings

Effective recognition of functional areas in arbitrary indoor scenes.

The detection model generalizes well across different datasets.

Demonstrated efficiency in scene understanding for robotic tasks.

Abstract

For robots that have the capability to interact with the physical environment through their end effectors, understanding the surrounding scenes is not merely a task of image classification or object recognition. To perform actual tasks, it is critical for the robot to have a functional understanding of the visual scene. Here, we address the problem of localizing and recognition of functional areas from an arbitrary indoor scene, formulated as a two-stage deep learning based detection pipeline. A new scene functionality testing-bed, which is complied from two publicly available indoor scene datasets, is used for evaluation. Our method is evaluated quantitatively on the new dataset, demonstrating the ability to perform efficient recognition of functional areas from arbitrary indoor scenes. We also demonstrate that our detection model can be generalized onto novel indoor scenes by cross validating it with the images from two different datasets.