his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Behavior recognition for segmentation of demonstrated tasks
Umeå universitet, Institutionen för datavetenskap.
Umeå universitet, Institutionen för datavetenskap.
2008 (English)In: IEEE SMC International Conference on Distributed Human-Machine Systems (DHMS), 2008, 228-234 p.Conference paper, (Refereed)
Resource type
Text
Abstract [en]

One common approach to the robot learning technique Learning From Demonstration, is to use a set of pre-programmed skills as building blocks for more complex tasks. One important part of this approach is recognition of these skills in a demonstration comprising a stream of sensor and actuator data. In this paper, three novel techniques for behavior recognition are presented and compared. The first technique is function-oriented and compares actions for similar inputs. The second technique is based on auto-associative neural networks and compares reconstruction errors in sensory-motor space. The third technique is based on S-Learning and compares sequences of patterns in sensory-motor space. All three techniques compute an activity level which can be seen as an alternative to a pure classification approach. Performed tests show how the former approach allows a more informative interpretation of a demonstration, by not determining "correct" behaviors but rather a number of alternative interpretations.

Place, publisher, year, edition, pages
2008. 228-234 p.
Keyword [en]
Learning from demonstration, Segmentation, Generalization, Sequence Learning, Auto-associative neural networks, S-Learning
National Category
Computer Science
Identifiers
URN: urn:nbn:se:his:diva-12145ISBN: 978-80-01-04027-0 (print)OAI: oai:DiVA.org:his-12145DiVA: diva2:1076493
Conference
IEEE SMC International Conference on Distributed Human-Machine Systems (DHMS)
Available from: 2008-03-19 Created: 2017-02-22 Last updated: 2017-04-18Bibliographically approved
In thesis
1. Cognition Rehearsed: Recognition and Reproduction of Demonstrated Behavior
Open this publication in new window or tab >>Cognition Rehearsed: Recognition and Reproduction of Demonstrated Behavior
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Robotövningar : Igenkänning och återgivande av demonstrerat beteende
Abstract [en]

The work presented in this dissertation investigates techniques for robot Learning from Demonstration (LFD). LFD is a well established approach where the robot is to learn from a set of demonstrations. The dissertation focuses on LFD where a human teacher demonstrates a behavior by controlling the robot via teleoperation. After demonstration, the robot should be able to reproduce the demonstrated behavior under varying conditions. In particular, the dissertation investigates techniques where previous behavioral knowledge is used as bias for generalization of demonstrations.

The primary contribution of this work is the development and evaluation of a semi-reactive approach to LFD called Predictive Sequence Learning (PSL). PSL has many interesting properties applied as a learning algorithm for robots. Few assumptions are introduced and little task-specific configuration is needed. PSL can be seen as a variable-order Markov model that progressively builds up the ability to predict or simulate future sensory-motor events, given a history of past events. The knowledge base generated during learning can be used to control the robot, such that the demonstrated behavior is reproduced. The same knowledge base can also be used to recognize an on-going behavior by comparing predicted sensor states with actual observations. Behavior recognition is an important part of LFD, both as a way to communicate with the human user and as a technique that allows the robot to use previous knowledge as parts of new, more complex, controllers.

In addition to the work on PSL, this dissertation provides a broad discussion on representation, recognition, and learning of robot behavior. LFD-related concepts such as demonstration, repetition, goal, and behavior are defined and analyzed, with focus on how bias is introduced by the use of behavior primitives. This analysis results in a formalism where LFD is described as transitions between information spaces. Assuming that the behavior recognition problem is partly solved, ways to deal with remaining ambiguities in the interpretation of a demonstration are proposed.

The evaluation of PSL shows that the algorithm can efficiently learn and reproduce simple behaviors. The algorithm is able to generalize to previously unseen situations while maintaining the reactive properties of the system. As the complexity of the demonstrated behavior increases, knowledge of one part of the behavior sometimes interferes with knowledge of another parts. As a result, different situations with similar sensory-motor interactions are sometimes confused and the robot fails to reproduce the behavior.

One way to handle these issues is to introduce a context layer that can support PSL by providing bias for predictions. Parts of the knowledge base that appear to fit the present context are highlighted, while other parts are inhibited. Which context should be active is continually re-evaluated using behavior recognition. This technique takes inspiration from several neurocomputational models that describe parts of the human brain as a hierarchical prediction system. With behavior recognition active, continually selecting the most suitable context for the present situation, the problem of knowledge interference is significantly reduced and the robot can successfully reproduce also more complex behaviors.

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University, 2012. 30 p.
Series
Report / UMINF, ISSN 0348-0542 ; 11.16
Keyword
Behavior Recognition, Learning and Adaptive Systems, Learning from Demonstration, Neurocomputational Modeling, Robot Learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:his:diva-12139 (URN)978-91-7459-349-5 (ISBN)
Public defence
2012-01-26, S1031, Norra Beteendevetarhuset, Umeå Universitet, 13:15 (English)
Opponent
Supervisors
Available from: 2017-04-18 Created: 2017-02-22 Last updated: 2017-04-18Bibliographically approved

Open Access in DiVA

No full text

Other links

https://www8.cs.umu.se/~thomash/reports/Billing%20Hellstrom%20DHMS%202008%20Proceedings.pdf

Search in DiVA

By author/editor
Billing, Erik A.Hellström, Thomas
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

Total: 34 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf