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  Learning to guide a robot via perceptions (1996) [7 citations — 3 self]

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by Anke Rieger
Procs. of the 3rd European Workshop on Planning. IOS
http://www-ai.informatik.uni-dortmund.de/DOKUMENTE/rieger_96b.ps.gz
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Abstract:

Abstract. We address the problem of guiding a robot in such a way, that it can decide, based on perceived sensor data, which future actions to choose, in order to reach a goal. In order to realize this guidance, the robot has access to a (probabilistic) automaton (PA), whose final states represent concepts, which have to be recognized in order to verify, that a goal has been achieved. The contribution of this work is to learn these PA's from classified sensor data of robot traces through known environments. Within this framework, we account for the uncertainties arising from ambiguous perceptions. 1

Citations

2103 A tutorial on hidden markov models and selected applications in speech recognition – Rabiner - 1989
370 Learning regular sets from queries and counterexamples – Angluin - 1987
129 Inference of Finite Automata Using Homing Sequences – Rivest, Schapire - 1993
125 Continuous Speech Recognition by Statistical Methods – Jelinek - 1976
35 Inferring finite automata with stochastic output functions and an application to map learning – Dean, Angluin, et al. - 1995
30 Learning concepts from sensor data of a mobile robot – Klingspor, Morik, et al. - 1996
23 Means-End Plan Recognition: Towards a Theory of Reactive Recognition – Rao - 1994
17 Learning action-oriented perceptual features for robot navigation – Morik, Rieger - 1993
11 Learning probabilistic automata and markov chains via queries – Tzeng - 1992
10 Integrating Marker-Passing and Problem Solving – Hendler - 1990
8 Lernen qualitativer Merkmale aus numerischen Robotersensordaten – Wessel - 1995
4 Learning finite automata using local distinguishing experiments – Shen - 1993
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