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Model-free learning from demonstration
Department of Computing Science, Umeå University, Umeå, Sweden.ORCID iD: 0000-0002-6568-9342
Department of Computing Science, Umeå University, Umeå, Sweden.
Department of Computing Science, Umeå University, Umeå, Sweden.
2010 (English)In: Proceedings of the 2nd International Conference on Agents and Artificial Intelligence: Volume 2 / [ed] Joaquim Filipe, Ana Fred and Bernadette Sharp, SciTePress, 2010, 62-71 p.Conference paper, (Refereed)
Resource type
Text
Abstract [en]

A novel robot learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated. PSL is a model-free prediction algorithm inspired by the dynamic temporal difference algorithm S-Learning. While S-Learning has previously been applied as a reinforcement learning algorithm for robots, PSL is here applied to a Learning from Demonstration problem. The proposed algorithm is evaluated on four tasks using a Khepera II robot. PSL builds a model from demonstrated data which is used to repeat the demonstrated behavior. After training, PSL can control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. PSL was able to successfully learn and repeat the first three (elementary) tasks, but it was unable to successfully repeat the fourth (composed) behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

Place, publisher, year, edition, pages
SciTePress, 2010. 62-71 p.
Keyword [en]
Learning from Demonstration, Prediction, Robot Imitation, Motor Control, Model-free Learning
National Category
Computer Science
Research subject
Computer and Information Science
Identifiers
URN: urn:nbn:se:his:diva-12150DOI: 10.5220/0002729500620071ISBN: 978-989-674-022-1 (print)OAI: oai:DiVA.org:his-12150DiVA: diva2:1076488
Conference
2nd International Conference on Agents and Artificial Intelligence (ICAART 2010), Valencia, Spain, January 22-24, 2010
Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2017-04-18
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

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Billing, Erik A.
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