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 workshop is therefore twofold.
Firstly, we will provide an overview of the state-of-the-art in affordance research and dissect open research challenges yielded thereof. Our speakers from ecological and perceptual psychology will be encouraged to ground these issues in human perceptual development and elaborate on their importance for robotics.
Secondly, we will encourage our speakers to debate whether computational models of affordance can potentially be advanced by adopting approaches that are more congruent with Gibson's original conception of direct perception. The question of whether the deep hierarchical models of the zeitgeist constitute a bridge between direct perception and internal representation provides a new vista in this interpretation of visual perception, and one which we aim to thoroughly explore.
The workshop organizers recently conducted a systematic literature survey on computational models of
affordance in robotics . All papers considered in the survey (146) were classified under a
detailed taxonomy that considers a comprehensive array of criteria relating to the study of affordances
in the literature. The full results of the study
have been published online
in a database that is continuously updated with new publications.
It is hoped that some of the results that have emerged from this survey can form the inception point of the thematic basis of the workshop. Have certain aspects of the computational modeling of affordance been neglected in favour of others? Are certain types of models superior to others in the study of affordances and, if so, under what circumstances? These are questions that the survey asks and hints at tentative answers to, but that the workshop can field the detailed and fruitful discussion of amongst world-leading cross-disciplinary experts, with the goal of fostering focused future research directions for ultimately solving the affordance learning problem in robotics.
 Philipp Zech, Simon Haller, Safoura Rezapour Lakani, Barry Ridge, Emre Ugur, Justus Piater, Computational models of affordance in robotics: a taxonomy and systematic classification. Adaptive Behavior, 25 (5), pp. 235–271, 2017. SAGE.
Participants are invited to submit contributions related to the aforementioned topics in one of the following categories:
All submissions will be peer-reviewed. Accepted papers will be presented during the workshop in a poster session. Outstanding papers will be presented as oral spotlight talks and invited to submit an extended version to a special issue of Adaptive Behavior on "Computational Models of Affordance for Robotics", a journal published by SAGE. The review process for the journal is independent from the review for this workshop.
Submissions must be in PDF following the R:SS two-column style available at:
and uploaded via the Microsoft CMT conference management system here: