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Functional Object Descriptors for Human Activity Modeling
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2314-2880
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-5750-9655
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, 1282-1289 p.Conference paper, Published paper (Refereed)
Abstract [en]

The ability to learn from human demonstration is essential for robots in human environments. The activity models that the robot builds from observation must take both the human motion and the objects involved into account. Object models designed for this purpose should reflect the role of the object in the activity - its function, or affordances. The main contribution of this paper is to represent object directly in terms of their interaction with human hands, rather than in terms of appearance. This enables the direct representation of object affordances/function, while being robust to intra-class differences in appearance. Object hypotheses are first extracted from a video sequence as tracks of associated image segments. The object hypotheses are encoded as strings, where the vocabulary corresponds to different types of interaction with human hands. The similarity between two such object descriptors can be measured using a string kernel. Experiments show these functional descriptors to capture differences and similarities in object affordances/function that are not represented by appearance.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 1282-1289 p.
Series
IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keyword [en]
Activity models, Functional object, Human activities, Human demonstrations, Human environment, Image segments, Object descriptors, Video sequences
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-138526DOI: 10.1109/ICRA.2013.6630736ISI: 000337617301042Scopus ID: 2-s2.0-84887281861ISBN: 978-1-4673-5641-1 (print)OAI: oai:DiVA.org:kth-138526DiVA: diva2:681275
Conference
2013 IEEE International Conference on Robotics and Automation, ICRA 2013; Karlsruhe; Germany; 6 May 2013 through 10 May 2013
Note

QC 20140107

Available from: 2013-12-19 Created: 2013-12-19 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.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2015:09
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-165680 (URN)
Public defence
2015-05-21, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20150504

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

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