Gibson’s theory of affordance, in its adherence to bottom-up direct perception, is antithetical to the top-down inferential models often proposed by modern robotics research purporting to tackle it. Such research assumes internal representation to be sacrosanct, but given current developments, to what extent can this assumption now be re-examined? The recently proposed sensorimotor contingency theory furthers the theoretical argument that internal representation is unnecessary, and its proof- of-concept application in robotics as well as the subsequent explosion in deep learning methodology sheds new light on the possibility of equipping robots with the capacity for directly perceiving their environments by exploiting correlated changes in their sensory inputs triggered by executing specific motor programs. This re-examination of direct perception is only one of several issues warranting scrutiny in current robotic affordance research.
The aim of this special issue is to highlight the relevance of Gibson’s notion of affordance for developmental and cognitive robotics. The issue is focused on contributions from the current panorama of robotics with an emphasis on theories from the ecological, cognitive, developmental and sensorimotor accounts.
We welcome submissions of all types (original research articles, reviews, short communications, and opinions) related to affordances and robotics, including but not limited to the following topics:
- Affordance learning
- Multimodal affordance learning
- Affordance perception and vision for affordances
- Perceptual learning and development
- Babbling and exploration
- Language and affordances
- Learning from observation and mirroring
- Self-organization of knowledge
- Deep learning of affordances
- Sensing physical properties
- Ecologically intuitive physics
- Bayesian learning of affordances
- Concept learning
- Symbol emergence
- Symbol grounding
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Sensorimotor contingency theory
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Behavior affording behavior
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Actions and functions in object perception
- Brain-body-environment systems
- Agent-environment systems
- Selective attention
- Self-supervised learning
This special issue is being released in conjunction with the 1st International Workshop on “Computational Models of Affordance in Robotics” to be held at the 2018 Robotics: Science and Systems conference in Pittsburgh, PA, USA. We encourage authors to submit early versions of their planned contributions to this workshop.
IMPORTANT DATES
Submission of papers: October 1, 2018
Reviewer’s feedback due: November 30, 2018
Revised submission due: January 31, 2019
Publication date: Summer 2019
HANDLING EDITOR
Philipp Zech, University of Innsbruck, Austria philipp.zech@uibk.ac.at
GUEST EDITORS
Barry Ridge, Jožef Stefan Institute, Slovenia Emre Ugur, Bogazici University, Turkey
4E Cognition Group 