Using Object Affordances to Improve Object Recognition

Castellini, Claudio; Tommasi, Tatiana; Noceti, Nicoletta; Odone, Francesca; Caputo, Barbara

doi:10.1109/tamd.2011.2106782

Cited by 72 publications

(33 citation statements)

References 29 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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Computational models of affordance in robotics: a taxonomy and systematic classification

et al. 2017

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“…In this case, the presence of a main axis in the object's shape can help the robot to generalize and transfer the acquired "rollable" property to other objects of similar shape. These ideas are at the base of research and experiments on the acquisition and exploitation of objects' affordances [74], [75], [76]; moreover, they could be easily integrated with symbol and language grounding [77]. Such experiments are out of the scope of this paper, but they are one of the natural follow-up to our work.…”

Section: A Learning Relates To Manipulationmentioning

confidence: 96%

Object Learning Through Active Exploration

Ivaldi

Nguyen

Lyubova

et al. 2014

IEEE Trans. Auton. Mental Dev.

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This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.

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“…In the literature, many features have been used to represent the target such as color [5], shape [25], texture [34] and kinematic features [6]. For a number of tracking applications, color is an optimal choice because of its descriptive power and the fact that color information is readily accessible in the image.…”

Section: Appearance Modelingmentioning

confidence: 99%

Adaptive NormalHedge for robust visual tracking

et al. 2015

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a b s t r a c tIn this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

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Using Object Affordances to Improve Object Recognition

Cited by 72 publications

References 29 publications

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

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

Object Learning Through Active Exploration

Adaptive NormalHedge for robust visual tracking

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