Object Affordances by Inferring on the Surroundings

Paola Ardon Ramirez, Subramanian Ramamoorthy, Katrin Solveig Lohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
49 Downloads (Pure)


Robotic cognitive manipulation methods aim to imitate the human-object interactive process. Most of the of the state-of-the-art literature explore these methods by focusing on the target object or on the robot's morphology, without including the surrounding environment. Most recent approaches suggest that taking into account the semantic properties of the surrounding environment improves the object recognition. When it comes to human cognitive development methods, these physical qualities are not only inferred from the object but also from the semantic characteristics of the surroundings. Thus the importance of affordances. In affordances, the representation of the perceived physical qualities of the objects gives valuable information about the possible manipulation actions. Hence, our research pursuits to develop a cognitive affordances map by (i) considering the object and the characteristics of the environment in which this object is more likely to appear, and (ii) achieving a learning mechanism that will intrinsically learn these affordances from self-experience.
Original languageEnglish
Title of host publication2018 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO)
Number of pages2
ISBN (Electronic)9781538680377
Publication statusPublished - 28 Jan 2019
EventIEEE Workshop on Advanced Robotics and its Social Impacts 2018: AI Empowered Robotics and its Impact on Society - Genova, Italy
Duration: 27 Sept 201829 Sept 2018

Publication series

NameIEEE Workshop on Advanced Robotics and its Social Impacts (ARSO)
ISSN (Electronic)2162-7576


WorkshopIEEE Workshop on Advanced Robotics and its Social Impacts 2018
Abbreviated titleARSO 2018
Internet address


  • Affordances
  • Grasping
  • Humanoid robot
  • Learning
  • Object recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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