Self-supervised online learning of basic object push affordances

Barry Ridge*, Ales Leonardis, Aleš Ude, Miha Deniša, Danijel Skočaj

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
87 Downloads (Pure)

Abstract

Continuous learning of object affordances in a cognitive robot is a challenging problem, the solution to which arguably requires a developmental approach. In this paper, we describe scenarios where robotic systems interact with household objects by pushing them using robot arms while observing the scene with cameras, and which must incrementally learn, without external supervision, both the effect classes that emerge from these interactions as well as a discriminative model for predicting them from object properties. We formalize the scenario as a multi-view learning problem where data co-occur over two separate data views over time, and we present an online learning framework that uses a self-supervised form of learning vector quantization to build the discriminative model. In various experiments, we demonstrate the effectiveness of this approach in comparison with related supervised methods using data from experiments performed using two different robotic platforms.

Original languageEnglish
Article number24
JournalInternational Journal of Advanced Robotic Systems
Volume12
Issue number3
Early online date1 Jan 2015
DOIs
Publication statusPublished - 1 Mar 2015

Keywords

  • Affordances
  • Cognitive and Developmental Robotics
  • Online Learning
  • Self-supervised Learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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