Structural Feature Extraction Based on Active Sensing Experiences

Nishide, Shun; Ogata, Tetsuya; Yokoya, R.; Komatani, Kazunori; Okuno, Hiroshi G.; Tani, Jun

doi:10.1109/icks.2008.9

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5] and [6], object recognition-style architectures are applied to the problem of detecting affordances in images. In [7], NNs are used to extract object features that inform object manipulation. CLIPort [8] finds affordances defined as motion start and end points in various scenes on basis of natural language instructions.…”

Section: Introductionmentioning

confidence: 99%

Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects

Arnold

Mami

Karashima

et al. 2023

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

We propose a novel system for action sequence planning based on a combination of affordance recognition and a neural forward model predicting the effects of affordance execution. By performing affordance recognition on predicted futures, we avoid reliance on explicit affordance effect definitions for multi-step planning. Because the system learns affordance effects from experience data, the system can foresee not just the canonical effects of an affordance, but also situation-specific side-effects. This allows the system to avoid planning failures due to such non-canonical effects, and makes it possible to exploit non-canonical effects for realising a given goal. We evaluate the system in simulation, on a set of test tasks that require consideration of canonical and non-canonical affordance effects.

show abstract

Section: Introductionmentioning

confidence: 99%

Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects

Arnold

Mami

Karashima

et al. 2023

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

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

Structural Feature Extraction Based on Active Sensing Experiences

Cited by 2 publications

References 7 publications

Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects

Recognising Affordances in Predicted Futures to Plan With Consideration of Non-Canonical Affordance Effects

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

Contact Info

Product

Resources

About