# Action Model Learning for Socio-Communicative Human Robot Interaction

1 ADELE [2016-2019] - Environnements et outils pour le Génie Logiciel Industriel [2016-2019]
LIG [2016-2019] - Laboratoire d'Informatique de Grenoble [2016-2019]
Abstract : Driven with the objective of rendering robots as socio-communicative, there has been a heightened interest towards researching techniques to endow robots with social skills and commonsense'' to render them acceptable. This social intelligence or commonsense'' of the robot is what eventually determines its social acceptability in the long run.Commonsense, however, is not that common. Robots can, thus, only learn to be acceptable with experience. However, teaching a humanoid the subtleties of a social interaction is not evident. Even a standard dialogue exchange integrates the widest possible panel of signs which intervene in the communication and are difficult to codify (synchronization between the expression of the body, the face, the tone of the voice, etc.). In such a scenario, learning the behavioral model of the robot is a promising approach. This learning can be performed with the help of AI techniques. This study tries to solve the problem of learning robot behavioral models in the Automated Planning and Scheduling (APS) paradigm of AI. In the domain of Automated Planning and Scheduling (APS), intelligent agents by virtue require an action model (blueprints of actions whose interleaved executions effectuates transitions of the system state) in order to plan and solve real world problems. During the course of this thesis, we introduce two new learning systems which facilitate the learning of action models, and extend the scope of these new systems to learn robot behavioral models. These techniques can be classified into the categories of non-optimal and optimal. Non-optimal techniques are more classical in the domain, have been worked upon for years, and are symbolic in nature. However, they have their share of quirks, resulting in a less-than-desired learning rate. The optimal techniques are pivoted on the recent advances in deep learning, in particular the Long Short Term Memory (LSTM) family of recurrent neural networks. These techniques are more cutting edge by virtue, and produce higher learning rates as well. This study brings into the limelight these two aforementioned techniques which are tested on AI benchmarks to evaluate their prowess. They are then applied to HRI traces to estimate the quality of the learnt robot behavioral model. This is in the interest of a long term objective to introduce behavioral autonomy in robots, such that they can communicate autonomously with humans without the need of wizard'' intervention.
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ARORA_2017_archivage.pdf
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• HAL Id : tel-01876157, version 1

### Citation

Ankuj Arora. Action Model Learning for Socio-Communicative Human Robot Interaction. Artificial Intelligence [cs.AI]. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAM081⟩. ⟨tel-01876157⟩

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