AfNet: The Affordance Network

Varadarajan, Karthik Mahesh; Vincze, Markus

doi:10.1007/978-3-642-37331-2_39

Cited by 21 publications

(9 citation statements)

References 11 publications

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“…In contrast to previous methods [99], [97], the authors used unsupervised clustering to segment grasping through 300 trials whereas SVM was used to learn affordance labels. Varadarajan et al [101], [102] developed a dataset to build knowledge ontologies similar to MIT ConceptNet [103] and KnowRob Semantic Map [104], but for household RGB-D images. They presented various affordance features such as grasp, material and structural.…”

Section: Affordance As a Contextmentioning

confidence: 99%

Visual Affordance and Function Understanding

2021

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Nowadays, robots are dominating the manufacturing, entertainment, and healthcare industries. Robot vision aims to equip robots with the capabilities to discover information, understand it, and interact with the environment, which require an agent to effectively understand object affordances and functions in complex visual domains. In this literature survey, first, “visual affordances” are focused on and current state-of-the-art approaches for solving relevant problems as well as open problems and research gaps are summarized. Then, sub-problems, such as affordance detection, categorization, segmentation, and high-level affordance reasoning, are specifically discussed. Furthermore, functional scene understanding and its prevalent descriptors used in the literature are covered. This survey also provides the necessary background to the problem, sheds light on its significance, and highlights the existing challenges for affordance and functionality learning.

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Section: Affordance As a Contextmentioning

confidence: 99%

Visual Affordance and Function Understanding

2021

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“…Containability Reasoning. Containability reasoning has been studied in the field of cognitive science [7]- [9] and computer vision [5], [10]- [12]. Liang et al [7] compare human judgements of containing relations between different objects with physical simulation results.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we focus on reasoning the open containability for granular material of an upright object. Based on the containability definition in [5], we propose the interaction-based definition of open containers:…”

Section: Introductionmentioning

confidence: 99%

Can I Pour into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations

Chirikjian

2020

Preprint

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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 results of generation of affordance filtration based topological maps in indoor environments is demonstrated through a simulation scenario described in Fig 2. In row 1 of the image, the objects providing the contain-ability affordance (denoted in red outlines) and ascertained using algorithm from [9,10] (working on depth images), predict the presence of a supportable surface (marked by a red line) that is added to the map (with ~ 0.7), even though this is not observed directly. As the robot returns to the kitchen table, as demonstrated in row 4, it sees a larger section of the kitchen table and the predicted supportable surface added to the map earlier with a low confidence value is now confirmed and augmented with a higher confidence value.…”

Section: Integrated Affordance Topology Mapsmentioning

confidence: 99%

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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AfNet: The Affordance Network

Cited by 21 publications

References 11 publications

Visual Affordance and Function Understanding

Visual Affordance and Function Understanding

Can I Pour into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations

Topological mapping for robot navigation using affordance features

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