Human-Robot Collaboration Using Industrial Robots

Antonelli, Dario; Bruno, Giulia

doi:10.2991/eame-17.2017.24

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The reuse of existing, comprehensive resources of these characteristics allow a high flexibility in the type of requests that can be directed towards the system. Furthermore, the domain is represented by making use of ontologies, which allow a detailed modelling of the scenario and reduce ambiguity between different agents (in this case, human and target system) [24]. Nevertheless, it has some limitations in terms of dialogue (it is only unidirectional at the moment) and the reported implementation does not include variants (i.e., synonyms) for the different terms involved in the interaction (e.g., objects).…”

Section: Related Workmentioning

confidence: 99%

KIDE4I: A Generic Semantics-Based Task-Oriented Dialogue System for Human-Machine Interaction in Industry 5.0

2022

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In Industry 5.0, human workers and their wellbeing are placed at the centre of the production process. In this context, task-oriented dialogue systems allow workers to delegate simple tasks to industrial assets while working on other, more complex ones. The possibility of naturally interacting with these systems reduces the cognitive demand to use them and triggers acceptation. Most modern solutions, however, do not allow a natural communication, and modern techniques to obtain such systems require large amounts of data to be trained, which is scarce in these scenarios. To overcome these challenges, this paper presents KIDE4I (Knowledge-drIven Dialogue framEwork for Industry), a semantic-based task-oriented dialogue system framework for industry that allows workers to naturally interact with industrial systems, is easy to adapt to new scenarios and does not require great amounts of data to be constructed. This work also reports the process to adapt KIDE4I to new scenarios. To validate and evaluate KIDE4I, it has been adapted to four use cases that are relevant to industrial scenarios following the described methodology, and two of them have been evaluated through two user studies. The system has been considered as accurate, useful, efficient, not demanding cognitively, flexible and fast. Furthermore, subjects view the system as a tool to improve their productivity and security while carrying out their tasks.

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Section: Related Workmentioning

confidence: 99%

KIDE4I: A Generic Semantics-Based Task-Oriented Dialogue System for Human-Machine Interaction in Industry 5.0

2022

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“…In general, for the natural language understanding component, most modern taskoriented dialogue systems are somewhat based in frames, which consist of a representation on the information to be provided as slots, to be filled with the information provided by the user [8] and supported by a knowledge base [9]. To assure the correct interpretation for a given command in this kind of dialogue systems, natural language technologies are used in several solutions in the literature [10,11]. Classical architectures made use of rules to detect the intent of the user and to perform slot filling, mainly semantic grammars, as it can be seen in [9], or templates [12].…”

Section: Related Workmentioning

confidence: 99%

“…In current approaches, ontologies have also been considered both for the natural language understanding and the dialogue management components of task-oriented dialogue systems, as they are a powerful tool that allows to define in detail the domain and reduce ambiguity between agents [10]. However, most dialogue systems that use ontologies found in the literature are limited to highly specific use cases and mainly to model the domain.…”

Section: Related Workmentioning

confidence: 99%

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TODO: A Core Ontology for Task-Oriented Dialogue Systems in Industry 4.0

Aceta¹,

Fernández²,

Soroa³

2021

Studies on the Semantic Web

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Nowadays, the demand in industry of dialogue systems to be able to naturally communicate with industrial systems is increasing, as they allow to enhance productivity and security in these scenarios. However, adapting these systems to different use cases is a costly process, due to the complexity of the scenarios and the lack of available data. This work presents the Task-Oriented Dialogue management Ontology (TODO), which aims to provide a core and complete base for semantic-based task-oriented dialogue systems in the context of industrial scenarios in terms of, on the one hand, domain and dialogue modelling and, on the other hand, dialogue management and tracing support. Furthermore, its modular structure, besides grouping specific knowledge in independent components, allows to easily extend each of the modules, attending the necessities of the different use cases. These characteristics allow an easy adaptation of the ontology to different use cases, with a considerable reduction of time and costs. So as to demonstrate the capabilities of the the ontology by integrating it in a task-oriented dialogue system, TODO has been validated in real-world use cases. Finally, an evaluation is also presented, covering different relevant aspects of the ontology.

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