Corpus ID: 23763602

Online adaptation to human engagement perturbations in simulated human- robot interaction using hybrid reinforcement learning

@inproceedings{Tsitsimis2017OnlineAT,
  title={Online adaptation to human engagement perturbations in simulated human- robot interaction using hybrid reinforcement learning},
  author={Theodore Tsitsimis and George Velentzas and Mehdi Khamassi and Costas S. Tzafestas},
  year={2017}
}
Dynamic uncontrolled human-robot interaction requires robots to be able to adapt to changes in the human’s behavior and intentions. Among relevant signals, non-verbal cues such as the human’s gaze can provide the robot with important information about the human’s current engagement in the task, and whether the robot should continue its current behavior or not. In a previous work [1] we proposed an active exploration algorithm for reinforcement learning where the reward function is the weighted… Expand

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