Detecting the functional similarities between tools using a hierarchical representation of outcomes

Sinapov, Jivko; Stoytchev, Alexander

doi:10.1109/devlrn.2008.4640811

Cited by 59 publications

(43 citation statements)

References 10 publications

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“…) grasping planar faces of object M SELF S 9 (Carvalho & Nolfi, 2016) traversability depth, haptic M SELF S 10 (Castellini et al, 2011) grasping SIFT BoW, contact joints M S B 11 (Çelikkanat et al, 2015) pushing, grasping, throwing, shaking depth, haptic, proprioceptive and audio M SEMI RR 12 (Chan et al, 2014) grasping pose, action-object relation M U RR 13 (Chang, 2015) cutting, painting edges, TSSC N S RR 14 (Chen et al, 2015) traversability RGB images, motor controls M S S 15 (Chu et al, 2016a) (Sinapov & Stoytchev, 2007) pulling, dragging changes in raw pixels M SELF S 113 (Sinapov & Stoytchev, 2008) pulling, dragging raw pixels, trajectories M SELF S 114 (Song et al, 2016) …”

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confidence: 99%

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

et al. 2017

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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]

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confidence: 99%

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

et al. 2017

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“…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

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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.

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“…For example, to learn the affordances of a tool, the methods described in [21] and [22] assume that the robot's sensorimotor data is cleanly partitioned according to the identity of each tool. Similarly, when categorizing objects as either containers or non-containers, the robot in [23] started with the implicit assumption that it already knows the identities of all objects that it has to interact with.…”

Section: B Roboticsmentioning

confidence: 99%

Grounded object individuation by a humanoid robot

Sinapov

Stoytchev

2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-This paper proposes a theoretical model that enables a robot to partition its unlabeled sensorimotor experience with different objects into discrete clusters, each corresponding to a specific object. To solve this object individuation problem, the robot was trained to detect whether two perceptual stimuli were produced by the same object or by two different objects. The model was tested using a large-scale experiment in which a humanoid robot explored 100 different objects by performing a variety of exploratory behaviors on them and detecting the resulting sensory feedback from several sensory modalities. The results show that with a small amount of prior training, the robot's model was able to successfully individuate the objects with a high degree of accuracy.

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Detecting the functional similarities between tools using a hierarchical representation of outcomes

Cited by 59 publications

References 10 publications

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

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

Learning Adjectives and Nouns from Affordances on the iCub Humanoid Robot

Grounded object individuation by a humanoid robot

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