Grounding the Interaction: Knowledge Management for Interactive Robots

Lemaignan, Séverin

doi:10.1007/s13218-013-0246-3

Cited by 12 publications

(10 citation statements)

References 88 publications

(140 reference statements)

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“…It is a Java software developed to showcase the role of semantic web technologies in IoT data management, based on Apache Jena. The robot itself is also a semantically enabled agent, it uses a "common sense" ontology and a knowledge base to reason about its 3D environment, as described in [5]. The knowledge specific to the robot relies on ontologies out of the scope of this paper, but its knowledge base can be extended with any ontology, including IoT-O (as it is done in this paper).…”

Section: Motivating Use Casementioning

confidence: 99%

IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

Seydoux

Drira

Hernandez

et al. 2016

Lecture Notes in Computer Science

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Section: Motivating Use Casementioning

confidence: 99%

IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

Seydoux

Drira

Hernandez

et al. 2016

Lecture Notes in Computer Science

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“…The development of Description Logics (DL) in the knowledge representation community, along with effective, practical tools (like reasoners) is a possible path forward, since DL semantics overlap to some extend with modal logics [2, chap. 4.2.2], and Description Logics have already been successfully used in robotics (see [28] for a review).…”

Section: Formal Epistemologymentioning

confidence: 99%

Mutual Modelling in Robotics

Lemaignan

Dillenbourg

2015

Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction

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Mutual modelling, the reciprocal ability to establish a mental model of the other, plays a fundamental role in human interactions. This complex cognitive skill is however difficult to fully apprehend as it encompasses multiple neuronal, psychological and social mechanisms that are generally not easily turned into computational models suitable for robots.This article presents several perspectives on mutual modelling from a range of disciplines, and reflects on how these perspectives can be beneficial to the advancement of social cognition in robotics. We gather here both basic tools (concepts, formalisms, models) and exemplary experimental settings and methods that are of relevance to robotics.This contribution is expected to consolidate the corpus of knowledge readily available to human-robot interaction research, and to foster interest for this fundamentally crossdisciplinary field.

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“…1), knowledge manipulation relies on a semantic blackboard: a central server (the ORO server [4]) stores knowledge as it is produced by each of the deliberative components. It conversely exposes a json-based RPC API to query the knowledge base [12].…”

Section: Knowledge Modelmentioning

confidence: 99%

“…Second remark, after some research (see appendix B of [19] for a detailed discussion) we have decided to represent neither the planning domain nor the resulting plans in the knowledge base: the planning domain (with task pre-and postconditions) is stored in a specific format, outside of the central declarative knowledge repository, and the plans are directly communicated to the robot controller. Thus, like many other cognitive architectures, we have independent declarative and procedural knowledge stores.…”

Section: Task Planningmentioning

confidence: 99%

Explicit knowledge and the deliberative layer: Lessons learned

Lemaignan

Alami²

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Over the last four years, we have been slowly ramping up explicit knowledge representation and manipulation in the deliberative and executive layers of our robots. Ranging from situation assessment to symbolic task planning, from verbal interaction to event-driven execution control, we have built up a knowledge-oriented architecture which is now used on a daily basis on our robots.This article presents our design choices, the articulations between the diverse deliberative components of the robot, and the strengths and weaknesses of this approach. We show that explicit knowledge management is not only a convenient tool from the software engineering point of view, but also pushes for a different, more semantic way to address the decision-making issue in autonomous robots. I. A KNOWLEDGE-ORIENTED ARCHITECTUREA. Towards the cognitive robot at LAAS Natural interaction and cooperation are on the (dare we say, short-term) agenda for the human-robot interaction community. They are keys to the broad class of interactive manipulation problems: several agents agree on a (more or less implicit) joint goal that requires some sort of cooperation to be successfully achieved. This class of problems involves both dialogue and manipulation and they are often initially underspecified: they require iterative and interactive resolution.Over the last years we have focused our efforts on identifying the cognitive prerequisites of these challenges, and giving them experimental reality on the robots: what is required for sentences like "Let's set the table together" to be understood by the robot, correctly interpreted in the spatial and temporal context of the interaction, and eventually transformed into a set of actions.We have chosen to tackle the challenge from several ends: human-aware navigation and motion planning [1], situation assessment coupled with motion planning [2], projection of "mightabilities" that anticipates what surrounding agents may do [3], and the design and deployment of a pervasive knowledge-oriented software architecture.The last item is the focus of this paper: what "knowledgeoriented" architecture means, and what its benefits and drawbacks are. B. Scope of the articleIn essence, this article portrays our use of explicit semantic interfaces to integrate several deliberative components into a decisional architecture for our robots. It reports on our experience with acquiring, managing and reusing grounded knowledge in the context of human-robot interaction.Unlike previous publications by the authors that introduced the deliberative modules of our robots independently from each other, this contribution is an account of the importance of explicit knowledge manipulation when integrating them into a large cognitive architecture.While we do not present experiments in this paper, the ideas and techniques we present all have been implemented and tested on several robots (see previously reported experiments in [4]-[7], [9]). Videos presenting some of these experimental results can be watched online at http://www.laas.fr/...

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Grounding the Interaction: Knowledge Management for Interactive Robots

Cited by 12 publications

References 88 publications

IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

Mutual Modelling in Robotics

Explicit knowledge and the deliberative layer: Lessons learned

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