Grounding of the Functional Object-Oriented Network in Industrial Tasks

TL;DR

This paper presents a preliminary activity recognition system for Industrie 4.0 that leverages functional object-oriented networks and linked data to improve data exchange and robot task execution in collaborative environments.

Contribution

It introduces a novel integration of FOON with linked data models for activity recognition in industrial collaborative robot tasks.

Findings

FOON can be applied to industrial use cases

Linked data models facilitate data exchange in LfD

Initial results show feasibility of the approach

Abstract

In this preliminary work, we propose to design an activity recognition system that is suitable for Industrie 4.0 (I4.0) applications, especially focusing on Learning from Demonstration (LfD) in collaborative robot tasks. More precisely, we focus on the issue of data exchange between an activity recognition system and a collaborative robotic system. We propose an activity recognition system with linked data using functional object-oriented network (FOON) to facilitate industrial use cases. Initially, we drafted a FOON for our use case. Afterwards, an action is estimated by using object and hand recognition systems coupled with a recurrent neural network, which refers to FOON objects and states. Finally, the detected action is shared via a context broker using an existing linked data model, thus enabling the robotic system to interpret the action and execute it afterwards. Our initial results show that FOON can be used for an industrial use case and that we can use existing linked data models in LfD applications.