KnowRobSIM — Game Engine-Enabled Knowledge Processing Towards Cognition-Enabled Robot Control

Haidu, Andrei; Bebler, Daniel; Bozcuoglu, Asil Kaan; Beetz, Michael

doi:10.1109/iros.2018.8593935

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…Recently, a second generation of the KnowRob system was introduced where the focus has shifted towards the integration of simulation and rendering techniques into a hybrid knowledge processing architecture (Beetz, Beßler, Haidu, et al 2018;Haidu et al 2018). The rational is to re-use components of the control program in virtual environments with physics and almost photorealistic rendering, and to acquire experiential knowledge from these sources.…”

Section: Knowrobmentioning

confidence: 99%

“…This was discussed, in more detail, in another work where Tenorth and Beetz argue that service robots should be able to cope with (often) shallow and symbolic instructions and to fill in the gaps to generate detailed, grounded, and (often) real-valued information needed for execution (Tenorth & Beetz, 2017). Recently, a second generation of the KnowRob system was introduced where the focus has shifted toward the integration of simulation and rendering techniques into a hybrid knowledge processing architecture (Beetz et al, 2018;Haidu et al 2018). The rational is to reuse components of the control program in virtual environments with physics and almost photorealistic rendering and to acquire experiential knowledge from these sources.…”

Section: Knowrobmentioning

confidence: 99%

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A review and comparison of ontology-based approaches to robot autonomy

Olivares-Alarcos

Beßler

Khamis

et al. 2019

The Knowledge Engineering Review

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Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

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Section: Knowrobmentioning

confidence: 99%

Section: Knowrobmentioning

confidence: 99%

A review and comparison of ontology-based approaches to robot autonomy

Olivares-Alarcos

Beßler

Khamis

et al. 2019

The Knowledge Engineering Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, as already hinted at in the introduction, imageschema-level formalisations are not intended to cover the low-level physics of a scenario. Rather, the force dynamic events that can be detected in, e.g., the physics simulations of robotics environments can trigger image-schematic primitives without a logical analysis of causation and force [27]. Therefore, the actual outcome of an open-ended formalisation of an everyday scenario such as 'cracking an egg' can only be determined if the precise force acting on the egg is known, and this can be read off the virtual enactment of the egg hitting the bowl in a simulation with precise physics.…”

Section: The Problem Of Force In Egg Crackingmentioning

confidence: 99%

“…However, it is possible to augment the handcrafted logical representation of image schemas with machine learning approaches detecting the satisfaction of image schematic states (see e.g. [27] for early work in this direction). Such a hybrid approach is therefore still based on the same fundamental principles of cognitively inspired modelling of events using image schemas, whilst avoiding both, handcrafted modelling of temporal event structure as well as logical modelling of causation and physics (instead relying on simulations).…”

Section: Introductionmentioning

confidence: 99%

Image Schema Combinations and Complex Events

et al. 2019

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Formal knowledge representation struggles to represent the dynamic changes within complex events in a cognitively plausible way. Image schemas, on the other hand, are spatiotemporal relationships used in cognitive science as building blocks to conceptualise objects and events on a high level of abstraction. In this paper, we explore this modelling gap by looking at how image schemas can capture the skeletal information of events and describe segmentation cuts essential for conceptualising dynamic changes. The main contribution of the paper is the introduction of a more systematic approach for the combination of image schemas with one another in order to capture the conceptual representation of complex concepts and events. To reach this goal we use the image schema logic ISL, and, based on foundational research in cognitive linguistics and developmental psychology, we motivate three different methods for the formal combination of image schemas: merge, collection, and structured combination. These methods are then used for formal event segmentation where the changes in image-schematic state generate the points of separation into individual scenes. The paper concludes with a demonstration of our methodology and an ontological analysis of the classic commonsense reasoning problem of 'cracking an egg.' Keywords Image schemas • Knowledge representation • Commonsense reasoning • Event structure • Ontology design patterns • Egg cracking problem This paper is an extended and improved version of [34].

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“…'the robot knows for sure that the object is on the floor'). In [4], physical reasoning is used to enhance knowledge about manipulation tasks, by acquiring simulated real world data in a game engine, but not to enhance execution in the presence of real humans. In contrast, our reasoner is able to manage occlusions and complex object interactions while the human is collaborating with a robot, also inferring the potential geometric inconsistency caused by the human's actions.…”

Section: Related Workmentioning

confidence: 99%

Simulation-based physics reasoning for consistent scene estimation in an HRI context

Sallami

Lemaignan

Clodic

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Reasoning about spatial and geometric relations between objects in a tabletop human-robot interaction is a challenge due to the perception not being always consistent: objects placed on a table seem to be slightly in the air; they overlap; they disappear due to occlusions. Yet, interpreting and anchoring perceptual data in a physically consistent estimation of the scene is a crucial ability for humans, and thus robots in HRI context. In this paper we present a simulationbased physics reasoner integrated in a lightweight situationassessment framework called Underworlds, that allows the robot to stabilize objects and build at run-time a consistent estimation of the scene, even for entirely hidden objects, while inferring the actions performed by its human partner.

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KnowRobSIM — Game Engine-Enabled Knowledge Processing Towards Cognition-Enabled Robot Control

Cited by 15 publications

References 18 publications

A review and comparison of ontology-based approaches to robot autonomy

A review and comparison of ontology-based approaches to robot autonomy

Image Schema Combinations and Complex Events

Simulation-based physics reasoning for consistent scene estimation in an HRI context

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