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 reexamined? 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 reexamination of direct perception is only one of several issues warranting scrutiny in current robotic affordance research.
The aim of this Research Topic is to highlight the relevance of Gibson's notion of affordance for developmental and cognitive robotics. The collection 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
- Bayesian learning of affordances
- Concept learning
- Symbol emergence
- Symbol grounding
- Sensorimotor contingency theory
- Behavior affording behavior
- Actions and functions in object perception
- Brain-body-environment systems
- Agent-environment systems
- Selective attention
- Self-supervised learning
- Sensing physical properties
- Ecologically intuitive physics
This Research Topic is being released in conjunction with the
2nd International Workshop on Computational Models of Affordance in Robotics to be held at ICRA 2019 in Montréal, Canada. We encourage authors to submit early versions of their planned contributions to this workshop.
