Affordance Research in Developmental Robotics: A Survey

Min, Huaqing; Yi, Chang’an; Luo, Ruiyan; Zhu, Jun; Bi, Sheng

doi:10.1109/tcds.2016.2614992

Cited by 82 publications

(43 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An important body of research comes from the developmental robotics field [16]. The core of these approaches is the representation and learning of actions and predicting the consequences of these over a set of objects.…”

Section: Related Workmentioning

confidence: 99%

Where can i do this? Geometric Affordances from a Single Example with the Interaction Tensor

Ochoa

Mayol-Cuevas

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

This paper develops and evaluates a new tensor field representation to express the geometric affordance of one object over another. We expand the well known bisector surface representation to one that is weight-driven and that retains the provenance of surface points with directional vectors. We also incorporate the notion of affordance keypoints which allow for faster decisions at a point of query and with a compact and straightforward descriptor. Using a single interaction example, we are able to generalize to previously-unseen scenarios; both synthetic and also real scenes captured with RGBD sensors. We show how our interaction tensor allows for significantly better performance over alternative formulations. Evaluations also include crowdsourcing comparisons that confirm the validity of our affordance proposals, which agree on average 84% of the time with human judgments, and which is 20-40% better than the baseline methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Where can i do this? Geometric Affordances from a Single Example with the Interaction Tensor

Ochoa

Mayol-Cuevas

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…Using this interpretation as a basis, we study affordances in full detail to construct our taxonomy (see Section 4). Min et al (2016) recently published a survey on the use of affordances in developmental robotics. Despite citing close to 200 papers, their study only classifies about 30 papers on this research topic that have so far been published.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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

“…Bio-inspired robots are usually developed using general network architectures of biological neural systems (Meyer and Guillot, 2008 ), synaptic plasticity (Grinke et al, 2015 ), correlation-based learning rule with synaptic scaling (Tetzlaff et al, 2011 ). Recent progresses in cognitive sciences and developmental neurobiology have promoted a new branch of robotics named “Cognitive Developmental Robotics (CDR)” (Asada et al, 2009 ; Asada, 2013 ; Min et al, 2016 ). Such robots behave in response to a dynamic environment by Spiking Neural Networks based controllers.…”

Section: Brain Natural Intelligence and Cognitive Developmental Robmentioning

confidence: 99%

Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future “Cognitive Developmental Robotics”

Faghihi

Moustafa

2017

Front. Neurorobot.

View full text Add to dashboard Cite

Affordance Research in Developmental Robotics: A Survey

Cited by 82 publications

References 117 publications

Where can i do this? Geometric Affordances from a Single Example with the Interaction Tensor

Where can i do this? Geometric Affordances from a Single Example with the Interaction Tensor

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

Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future “Cognitive Developmental Robotics”

Contact Info

Product

Resources

About