Human-Robot Interactive Learning Architecture using Ontologies and Symbol Manipulation

Angleraud, Alexandre; Houbre, Quentin; Kyrki, Ville; Pieters, Roel

doi:10.1109/roman.2018.8525580

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…The architecture of our system is based on previous developments on knowledge-based planning for human-robot collaborative tasks ( Angleraud et al, 2018 ). One crucial difference is that no high-level planner is utilized and autonomous robot actions, as inferred from the knowledge base and reasoning, are excluded.…”

Section: Methodsmentioning

confidence: 99%

Coordinating Shared Tasks in Human-Robot Collaboration by Commands

et al. 2021

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Human-robot collaboration is gaining more and more interest in industrial settings, as collaborative robots are considered safe and robot actions can be programmed easily by, for example, physical interaction. Despite this, robot programming mostly focuses on automated robot motions and interactive tasks or coordination between human and robot still requires additional developments. For example, the selection of which tasks or actions a robot should do next might not be known beforehand or might change at the last moment. Within a human-robot collaborative setting, the coordination of complex shared tasks, is therefore more suited to a human, where a robot would act upon requested commands.In this work we explore the utilization of commands to coordinate a shared task between a human and a robot, in a shared work space. Based on a known set of higher-level actions (e.g., pick-and-placement, hand-over, kitting) and the commands that trigger them, both a speech-based and graphical command-based interface are developed to investigate its use. While speech-based interaction might be more intuitive for coordination, in industrial settings background sounds and noise might hinder its capabilities. The graphical command-based interface circumvents this, while still demonstrating the capabilities of coordination. The developed architecture follows a knowledge-based approach, where the actions available to the robot are checked at runtime whether they suit the task and the current state of the world. Experimental results on industrially relevant assembly, kitting and hand-over tasks in a laboratory setting demonstrate that graphical command-based and speech-based coordination with high-level commands is effective for collaboration between a human and a robot.

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Section: Methodsmentioning

confidence: 99%

Coordinating Shared Tasks in Human-Robot Collaboration by Commands

et al. 2021

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“…Thanks to these representations a large possibility in terms of reasoning mechanisms and, manipulation of data and metadata are possible [4]. For all these advantages, the ontological models are adopted in the fields of cognitive robotics and that of artificial intelligence.…”

Section: Ontology Capabilities and Usagementioning

confidence: 99%

Human-Robot Multilingual Verbal Communication – The Ontological knowledge and Learning-based Models

Qbadou¹,

Salhi²,

Mansouri³

et al. 2020

Adv. sci. technol. eng. syst. j.

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In their verbal interactions, humans are often afforded with language barriers and communication problems and disabilities. This problem is even more serious in the fields of education and health care for children with special needs. The use of robotic agents, notably humanoids integrated within human groups, is a very important option to face these limitations. Many scientific research projects attempt to provide solutions to these communication problems by integrating intelligent robotic agents with natural language communication abilities. These agents will thus be able to help children suffering from verbal communication disorders, more particularly in the fields of education and medicine. In addition, the introduction of robotic agents into the child's environment creates stimulating effects for more verbal interaction. Such stimulation may improve their ability to interact with pairs. In this paper, we propose a new approach for the human-robot multilingual verbal interaction based on hybridization of recent and performant approach on translation machine system consisting of neural network model reinforced by a large distributed domain-ontology knowledge database. We have constructed this ontology by crawling a large number of educational web sites providing multilingual parallel texts and speeches. Furthermore, we present the design of augmented LSTM neural Network models and their implementation to permit, in learning context, communication between robots and children using multiple natural languages. The model of a general ontology for multilingual verbal communication is produced to describe a set of linguistic and semantic entities, their properties and relationships. This model is used as an ontological knowledge base representing the verbal communication of robots with children.

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“…This scenario was implemented with an Amazon Echo TM with Alexa to add new knowledge to Ontologenius and query it verbally. It can be used to extend the examples presented with the human-robot interactive learning architecture of Angleraud et al [29] in which a human teaches a robot that spaghetti are pasta so that the robot infers how to cook them knowing how to cook pasta.…”

Section: B Do the Birds Fly?mentioning

confidence: 99%

Ontologenius: A long-term semantic memory for robotic agents

Sarthou

Clodic

Alami

2019

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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In this paper we present Ontologenius, a semantic knowledge storage and reasoning framework for autonomous robots. More than a classic ontology software to query a knowledge base and a first-order internal logic as it can be done for web-semantics, we propose with Ontologenius features adapted to a robotic use including human-robot interaction. We introduce the ability to modify the knowledge base during execution, whether through dialogue or geometric reasoning, and keep these changes even after the robot is powered off. Since Ontologenius was developed to be used by a robot which interacts with humans, we have endowed the system with ability to perform attributes and properties generalization and with the possibility to model and estimate the semantic memory of a human partner and to implement theory of mind processes. This paper presents the architecture and the main features of Ontologenius as well as examples of its use in robotics applications.

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Human-Robot Interactive Learning Architecture using Ontologies and Symbol Manipulation

Cited by 6 publications

References 17 publications

Coordinating Shared Tasks in Human-Robot Collaboration by Commands

Coordinating Shared Tasks in Human-Robot Collaboration by Commands

Human-Robot Multilingual Verbal Communication – The Ontological knowledge and Learning-based Models

Ontologenius: A long-term semantic memory for robotic agents

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