Affordances in Psychology, Neuroscience, and Robotics: A Survey

Jamone, Lorenzo; Uğur, Emre; Cangelosi, Angelo; Fadiga, Luciano; Bernardino, Alexandre; Piater, Justus; Santos-Victor, José

doi:10.1109/tcds.2016.2594134

Cited by 135 publications

(102 citation statements)

References 243 publications

(252 reference statements)

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“…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%

“…Recently Thill et al (2013) and Jamone et al (2016) published two thorough reviews on both theories and computational models of affordance (Thill et al, 2013) as well as the use of affordances in psychology, neuroscience and robotics . Their work shows that the field of affordance related research is truly both vast and diverse, and has been growing rapidly since the early 2000's.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…In robotics, there is an increasingly large body of literature in affordances and computational models of affordances. A recent overview is given in [2]. The idea behind robots learning object affordances, or action possibilities, is that knowledge acquired through self-exploration and interaction with the world can be used to make inferences in new scenarios and tasks, such as prediction [16], tool use [17]- [20], and planning [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Learning at the ends: From hand to tool affordances in humanoid robots

Saponaro

Vicente

Dehban

et al. 2017

2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactions is unfeasible, learning from experience is required, posing the challenge of collecting a large amount of experiences (i.e., training data). Typically, a manipulative robot operates on external objects by using its own hands (or similar end-effectors), but in some cases the use of tools may be desirable; nevertheless, it is reasonable to assume that while a robot can collect many sensorimotor experiences using its own hands, this cannot happen for all possible human-made tools.Therefore, in this paper we investigate the developmental transition from hand to tool affordances: what sensorimotor skills that a robot has acquired with its bare hands can be employed for tool use? By employing a visual and motor imagination mechanism to represent different hand postures compactly, we propose a probabilistic model to learn hand affordances, and we show how this model can generalize to estimate the affordances of previously unseen tools, ultimately supporting planning, decision-making and tool selection tasks in humanoid robots. We present experimental results with the iCub humanoid robot, and we publicly release the collected sensorimotor data in the form of a hand posture affordances dataset.

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“…In most of the works within the affordance literature [4] an anthropomorphic robot or a robotic arm interacts with one or more objects to reproduce effects on it, learning their affordances. Initially the robot is endowed with a set of actions.…”

Section: Related Workmentioning

confidence: 99%

Iterative affordance learning with adaptive action generation

Maestre¹,

Mukhtar²,

Gonzales³

et al. 2017

2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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Abstract-A robot designer can provide a robot with knowledge to perform tasks on an environment. However, this approach can limit the achievement of future tasks executed by the robot. Providing it with the ability to develop its own skills paves the way for robots that are not limited by design. In this work a task consists in reproducing a given set of effects on an object. A robot must accomplish this task with limited information about the object, learning affordances to reproduce the effects, increasing this information throughout consecutive interactions with the object. We propose a method named Adaptive Affordance Learning (A 2 L) which endows a robot with the capacity to learn affordances associated to an object, both adapting the robot's actions to the object position, and increasing the robot's information about the object when needed. This paper presents two main contributions: first, an online adaption of the robot actions to interact with the object, decomposing each action into a sequence of movements, adapting each movement, in a close loop, to the object position; and second, to increase the information about the object, we propose an iterative process that alternates between (1) exploration of the environment interacting with the object, (2) affordance acquisition and (3) affordance validation. These contributions are assessed in two experiments where a simulated Baxter robot learns to push a box to different positions on a table.

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Affordances in Psychology, Neuroscience, and Robotics: A Survey

Cited by 135 publications

References 243 publications

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

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

Learning at the ends: From hand to tool affordances in humanoid robots

Iterative affordance learning with adaptive action generation

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