Functional Object-Oriented Network for Manipulation Learning
This addresses the challenge of robot manipulation learning by providing a flexible, knowledge-based approach for generating adaptive motion sequences from multiple sources, though it appears incremental in building on existing graphical models for task representation.
The paper tackles the problem of enabling robots to perform manipulation tasks by introducing a functional object-oriented network (FOON), a structured knowledge representation learned from observing object state changes and human manipulations, which allows robots to decipher task goals and generate motion sequences, demonstrated in a simulated environment.
This paper presents a novel structured knowledge representation called the functional object-oriented network (FOON) to model the connectivity of the functional-related objects and their motions in manipulation tasks. The graphical model FOON is learned by observing object state change and human manipulations with the objects. Using a well-trained FOON, robots can decipher a task goal, seek the correct objects at the desired states on which to operate, and generate a sequence of proper manipulation motions. The paper describes FOON's structure and an approach to form a universal FOON with extracted knowledge from online instructional videos. A graph retrieval approach is presented to generate manipulation motion sequences from the FOON to achieve a desired goal, demonstrating the flexibility of FOON in creating a novel and adaptive means of solving a problem using knowledge gathered from multiple sources. The results are demonstrated in a simulated environment to illustrate the motion sequences generated from the FOON to carry out the desired tasks.