S.A. Khan, S. Arif, and L. Bölöni

Towards learning movement in dense crowds for a socially-aware mobile robot


Cite as:

S.A. Khan, S. Arif, and L. Bölöni. Towards learning movement in dense crowds for a socially-aware mobile robot. In Workshop on Adaptive Learning Agents (ALA-2014), May 2014.

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Abstract:

Robots moving in a crowd occasionally reach situations where they need to decide whether to give way to a human or not, a situation we call a \em micro-conflict and model with a two player game. We collect data from a robot controlled by a human operator and use three different supervised learning algorithms (random forest, SVM and neuro-evolution) to create a decision maker module which imitates the human operator's behavior in micro-conflicts. Results show that the neuro-evolution based decision-maker gives the best performance under scenarios with various crowd density and urgency. In addition, we found that the neuro-evolution method generalizes better to environments very different from those in the training set.

BibTeX:

@inproceedings{Khan-2014-ALA,
   title = "Towards learning movement in dense crowds for a socially-aware mobile robot",
   author = "S.A. Khan and S. Arif and L. B{\"o}l{\"o}ni",
   booktitle = "Workshop on Adaptive Learning Agents (ALA-2014)",
   year = "2014",
   month = "May",
   abstract = {
   Robots moving in a crowd occasionally reach situations where they need to decide whether to give way to a human or not, a situation we call a {\em micro-conflict} and model with a two player game. We collect data from a robot controlled by a human operator and use three different supervised learning algorithms (random forest, SVM and neuro-evolution) to create a decision maker module which imitates the human operator's behavior in micro-conflicts. Results show that the neuro-evolution based decision-maker gives the best performance under scenarios with various crowd density and urgency. In addition, we found that the neuro-evolution method generalizes better to environments very different from those in the training set.
},
}

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