Knowledge Retrieval using Functional Object-Oriented Network

TL;DR

This paper presents a graph-based knowledge representation called FOON for robotic task planning, demonstrating its ability to generate motion sequences for task execution in simulation, thereby enhancing robot autonomy.

Contribution

It introduces FOON as a novel, adaptable graph-based knowledge structure for symbolic task planning in robotics, enabling effective motion sequence retrieval from diverse knowledge sources.

Findings

Successfully generated motion sequences for tasks in simulation.

Demonstrated FOON's adaptability in knowledge integration.

Improved robot task success rate with FOON-based planning.

Abstract

Robots can complete all human-performed tasks, but due to their current lack of knowledge, some tasks still cannot be completed by them with a high degree of success. However, with the right knowledge, these tasks can be completed by robots with a high degree of success, reducing the amount of human effort required to complete daily tasks. In this paper, the FOON, which describes the robot action success rate, is discussed. The functional object-oriented network (FOON) is a knowledge representation for symbolic task planning that takes the shape of a graph. It is to demonstrate the adaptability of FOON in developing a novel and adaptive method of solving a problem utilizing knowledge obtained from various sources, a graph retrieval methodology is shown to produce manipulation motion sequences from the FOON to accomplish a desired aim. The outcomes are illustrated using motion sequences created by the FOON to complete the desired objectives in a simulated environment.