Goal emulation and planning in perceptual space using learned affordances

Uğur, Emre; Öztop, Erhan; Şahi̇n, Erol

doi:10.1016/j.robot.2011.04.005

Cited by 76 publications

(71 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a result of this classification we then provide a comprehensive and qualitative discussion on existing research to identify both promising and potentially futile directions as well as open problems and research questions to be addressed in future (see Section 6). Though existing studies also give similar discussions (Ugur et al, 2011;Horton et al, 2012;Thill et al, 2013;Jamone et al, 2016), our discussion is distinct in being motivated by a quantitative study.…”

Section: Introductionsupporting

confidence: 61%

“…We see this as an unfortunate deficit in the field, as it is presently extremely difficult to quantitatively compare different computational models of affordance to study their competitiveness. Ugur et al (2011) published an early, coarse classification of various applications of the concept of affordance in robotics. In total, their classification considered 16 published works by studying their applied learning schemes and internal representations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational models of affordance in robotics: a taxonomy and systematic classification

et al. 2017

Self Cite

View full text Add to dashboard Cite

J. J. Gibson's concept of affordance, one of the central pillars of ecological psychology, is a truly remarkable idea that provides a concise theory of animal perception predicated on environmental interaction. It is thus not surprising that this idea has also found its way into robotics research as one of the underlying theories for action perception. The success of the theory in this regard has meant that existing research is both abundant and diffuse by virtue of the pursuit of multiple different paths and techniques with the common goal of enabling robots to learn, perceive and act upon affordances. Up until now there has existed no systematic investigation of existing work in this field. Motivated by this circumstance, in this article we begin by defining a taxonomy for computational models of affordances rooted in a comprehensive analysis of the most prominent theoretical ideas of import in the field. Subsequently, after performing a systematic literature review, we provide a classification of existing research within our proposed taxonomy. Finally, by both quantitatively and qualitatively assessing the data resulting from the classification process, we highlight gaps in the research terrain and outline open questions for the investigation of affordances in robotics that we believe will help inform future work, prioritize research goals, and potentially advance the field towards greater robot autonomy.Prepared using sagej.cls [Version: 2015/06/09 v1.01]

show abstract

Section: Introductionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Computational models of affordance in robotics: a taxonomy and systematic classification

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar to the work presented here, Ugur et al [15] learn action-outcome relations and apply these for functional categorization. Object-related sensory data are represented as 43-dimensional vectors, which include object position, visibility and shape.…”

Section: Related Workmentioning

confidence: 80%

“…Object-related contingencies are tightly connected to the notion of object affordances [11], [12], [13], [14], [15] that are defined by the set of actions that can be applied to an object. An object-related eSMC encodes or predicts an outcome of a certain action performed on an object.…”

Section: Introductionmentioning

confidence: 99%

Interactive object classification using sensorimotor contingencies

Högman

Björkman

Kragić

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification.

show abstract

“…Note that an agent would require more of such relations on different objects and behaviours to learn more general affordance relations and to conceptualize over its sensorimotor experiences. During the last decade, similar formalizations of affordances proved to be very practical with successful applications to domains such as navigation [15], manipulation [16,17,18,19,20], conceptualization and language [5,4], planning [18], imitation and emulation [12,18,4], tool use [21,22,13] and vision [4]. A notable one with a notion of affordances similar to ours is presented by Montesano et al [23,24].…”

Section: Related Studiesmentioning

confidence: 72%

Learning Adjectives and Nouns from Affordances on the iCub Humanoid Robot

Yürüten

Uyanik

Çalışkan

et al. 2012

From Animals to Animats 12

View full text Add to dashboard Cite

Abstract. This article studies how a robot can learn nouns and adjectives in language. Towards this end, we extended a framework that enabled robots to learn affordances from its sensorimotor interactions, to learn nouns and adjectives using labeling from humans. Specifically, an iCub humanoid robot interacted with a set of objects (each labeled with a set of adjectives and a noun) and learned to predict the effects (as labeled with a set of verbs) it can generate on them with its behaviors. Different from appearance-based studies that directly link the appearances of objects to nouns and adjectives, we first predict the affordances of an object through a set of Support Vector Machine classifiers which provided a functional view of the object. Then, we learned the mapping between these predicted affordance values and nouns and adjectives. We evaluated and compared a number of different approaches towards the learning of nouns and adjectives on a small set of novel objects.The results show that the proposed method provides better generalization than the appearance-based approaches towards learning adjectives whereas, for nouns, the reverse is the case. We conclude that affordances of objects can be more informative for (a subset of) adjectives describing objects in language.

show abstract

Goal emulation and planning in perceptual space using learned affordances

Cited by 76 publications

References 73 publications

Computational models of affordance in robotics: a taxonomy and systematic classification

Computational models of affordance in robotics: a taxonomy and systematic classification

Interactive object classification using sensorimotor contingencies

Learning Adjectives and Nouns from Affordances on the iCub Humanoid Robot

Contact Info

Product

Resources

About