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Recognizing Object Affordances in Terms of Spatio-Temporal Object-Object Relationships
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2314-2880
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (Computer Vision and Active Perception (CVAP) Lab)
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-5750-9655
2014 (English)In: Humanoid Robots (Humanoids), 2014 14th IEEE-RAS International Conference on, IEEE conference proceedings, 2014, 52-58 p.Conference paper (Refereed)
Abstract [en]

In this paper we describe a probabilistic framework that models the interaction between multiple objects in a scene.We present a spatio-temporal feature encoding pairwise interactions between each object in the scene. By the use of a kernel representation we embed object interactions in a vector space which allows us to define a metric comparing interactions of different temporal extent. Using this metric we define a probabilistic model which allows us to represent and extract the affordances of individual objects based on the structure of their interaction. In this paper we focus on the presented pairwise relationships but the model can naturally be extended to incorporate additional cues related to a single object or multiple objects. We compare our approach with traditional kernel approaches and show a significant improvement.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 52-58 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-158008DOI: 10.1109/HUMANOIDS.2014.7041337ScopusID: 2-s2.0-84945185392OAI: diva2:773367
International Conference on Humanoid Robots,November 18-20th 2014, Madrid, Spain

QC 20141223

Available from: 2014-12-18 Created: 2014-12-18 Last updated: 2015-05-04Bibliographically approved
In thesis
1. Action Recognition for Robot Learning
Open this publication in new window or tab >>Action Recognition for Robot Learning
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis builds on the observation that robots cannot be programmed to handle any possible situation in the world. Like humans, they need mechanisms to deal with previously unseen situations and unknown objects. One of the skills humans rely on to deal with the unknown is the ability to learn by observing others. This thesis addresses the challenge of enabling a robot to learn from a human instructor. In particular, it is focused on objects. How can a robot find previously unseen objects? How can it track the object with its gaze? How can the object be employed in activities? Throughout this thesis, these questions are addressed with the end goal of allowing a robot to observe a human instructor and learn how to perform an activity. The robot is assumed to know very little about the world and it is supposed to discover objects autonomously. Given a visual input, object hypotheses are formulated by leveraging on common contextual knowledge often used by humans (e.g. gravity, compactness, convexity). Moreover, unknown objects are tracked and their appearance is updated over time since only a small fraction of the object is visible from the robot initially. Finally, object functionality is inferred by looking how the human instructor is manipulating objects and how objects are used in relation to others. All the methods included in this thesis have been evaluated on datasets that are publicly available or that we collected, showing the importance of these learning abilities.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. v, 38 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2015:09
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
urn:nbn:se:kth:diva-165680 (URN)
Public defence
2015-05-21, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00 (English)

QC 20150504

Available from: 2015-05-04 Created: 2015-04-29 Last updated: 2015-05-04Bibliographically approved

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