AfRob: The affordance network ontology for robots

Varadarajan, Karthik Mahesh; Vincze, Markus

doi:10.1109/iros.2012.6386232

Cited by 38 publications

(21 citation statements)

References 6 publications

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“…For example, a cup provides the contain-ability affordance that enables it to contain a solid or liquid within its structural bounds. We borrow from [9,10], the ontology of affordance features for our work in this paper.…”

Section: Algorithmmentioning

confidence: 99%

“…The AfRob extension [10] to AfNet provides linkage for robotic applications. One of the primary schema in AfRob is top down search refinement.…”

Section: B Afrob and Topological Mappingmentioning

confidence: 99%

“…Drawing from [10], semantic affordance is dependent on co-occurrence or typical pose of objects. Semantic affordances define the Subject-Object relationships for the functional affordances (structural and material).…”

Section: Semantic Affordance Features In Afrobmentioning

confidence: 99%

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Topological mapping for robot navigation using affordance features

Varadarajan

2015

2015 6th International Conference on Automation, Robotics and Applications (ICARA)

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Affordance features are being increasingly used for a number of robotic applications. An open affordance framework called AfNet defines over 250 objects in terms of 35 affordance features that are grounded in visual perception algorithms. While AfNet is intended for usage with cognitive visual recognition systems, an extension to the framework, called AfRob delivers an affordance based ontology targeted at robotic applications. Applications in which AfRob has been used include (a) top down task driven saliency detection (b) cognitive object recognition (c) task based object grasping and manipulation. In this paper, we use AfRob as base for building topological maps intended for robotic navigation. Traditional approaches to robotic navigation use metric maps or topological maps or hybrid systems that combine the two approaches at different levels of resolution or granularity. While metric and grid based maps provide high accuracy results for optimal path planning schemes, they require high space-time requirements for computation and storage, reducing real-time applicability. On the other hand, topological maps being graph based abstract structures are extremely light and convenient for goal driven navigation, but suffer from lack of resolution, poor self-localization and loop closing. Both approaches show severe restrictions in the case of dynamic environments in which objects which serve as features for the map building procedure are moved or removed from the scene across the time period of usage of the robot. This paper presents a novel approach to topological map building that takes into account affordance features that can help build lightweight, high-resolution, holistic and cognitive maps by predicting positional and functional characteristics of unseen objects. In addition, these features enable a cognitive approach to handling dynamic scene content, providing for enhanced loop closing and self-localization over traditional topological map building. These features also offer cues to place learning and functional room unit classification thereby providing for superior task based path planning. Since these features are easy to detect, fast building of maps is possible. Results on synthetic and real scenes demonstrate the benefits of the proposed approach.

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Section: Algorithmmentioning

confidence: 99%

“…The AfRob extension [10] to AfNet provides linkage for robotic applications. One of the primary schema in AfRob is top down search refinement.…”

Section: B Afrob and Topological Mappingmentioning

confidence: 99%

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Topological mapping for robot navigation using affordance features

Varadarajan

2015

2015 6th International Conference on Automation, Robotics and Applications (ICARA)

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“…However, both the aforementioned works fail to generalize to unknown (untrained) objects, whilst requiring adequate knowledge of the working environment of the robot. In other robotics applications ontologies are utilized with view to provide a more compact representation of the 3D objects (Varadarajan & Vincze, 2012) and to study the relation between specific models and the corresponding robot action (Modayil & Kuipers, 2007). In this paper, the ontologies are utilized in an holistic manner with aim to establish a novel knowledge domain focusing on industrial object manipulation.…”

Section: Ontologiesmentioning

confidence: 99%

What, Where and How? Introducing pose manifolds for industrial object manipulation

Kouskouridas

Amanatiadis

Chatzichristofis

et al. 2015

Expert Systems with Applications

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“…Some recent work [6] [8] [13] in computer vision and robotics extended this concept of affordance and applied it to object classification and object manipulation. Affordances can be associated with parts of an object as, for example in the work done by Varadarajan [16] [15], where predefined base affordances are associated with surface types. In our work, we build models that inform inference in an extension of Gibson's original ideas about direct perception [3] [5].…”

Section: Introductionmentioning

confidence: 99%

The Aspect Transition Graph: An Affordance-Based Model

Sen

Learned-Miller

et al. 2015

Computer Vision - ECCV 2014 Workshops

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Abstract. In this work we introduce the Aspect Transition Graph (ATG), an affordance-based model that is grounded in the robot's own actions and perceptions. An ATG summarizes how observations of an object or the environment changes in the course of interaction. Through the Robonaut 2 simulator, we demonstrate that by exploiting these learned models the robot can recognize objects and manipulate them to reach certain goal state.

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AfRob: The affordance network ontology for robots

Cited by 38 publications

References 6 publications

Topological mapping for robot navigation using affordance features

Topological mapping for robot navigation using affordance features

What, Where and How? Introducing pose manifolds for industrial object manipulation

The Aspect Transition Graph: An Affordance-Based Model

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