Discovering and Manipulating Affordances - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

Discovering and Manipulating Affordances

Abstract

Reasoning jointly on perception and action requires to interpret the scene in terms of the agent's own potential capabilities. We propose a Bayesian architecture for learning sensorimotor representations from the interaction between perception, action, and salient changes generated by robot actions. This connects these three elements in a common representation: affordances. In this paper, we are working towards a richer representation and formalization of affordances. Current experimental analysis shows the qualitative and quantitative aspects of affordances. In addition, our formalization motivates several experiments for exploring hypothetical operations between learned affordances. In particular, we infer affordances of composite objects, based on prior knowledge on the affordances of the elementary objects.
Fichier principal
Vignette du fichier
finaliser2016.pdf (2.09 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01391427 , version 1 (03-11-2016)

Identifiers

  • HAL Id : hal-01391427 , version 1

Cite

Omar Ricardo Chavez-Garcia, Mihai Andries, Pierre Luce-Vayrac, Raja Chatila. Discovering and Manipulating Affordances. International Symposium on Experimental Robotics (ISER 2016), Oct 2016, Tokyo, Japan. ⟨hal-01391427⟩
139 View
192 Download

Share

Gmail Facebook Twitter LinkedIn More