@inproceedings{Senft2017a,
  abstract = {A key challenge of HRI is allowing robots to be adaptable, especially as robots are expected to penetrate society at large and to interact in unexpected environments with non- technical users. One way of providing this adaptability is to use Interactive Machine Learning, i.e. having a human supervisor included in the learning process who can steer the action selection and the learning in the desired direction. We ran a study exploring how people use numeric rewards to evaluate a robot's behaviour and guide its learning. From the results we derive a number of challenges when designing learning robots: what kind of input should the human provide? How should the robot communicate its state or its intention? And how can the teaching process by made easier for human supervisors?},
  address = {Vienna, Austria},
  author = {Senft, Emmanuel and Lemaignan, S{\'{e}}verin and Baxter, Paul E. and Belpaeme, Tony},
  booktitle = {Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction - HRI '17},
  doi = {10.1145/3029798.3038385},
  isbn = {9781450348850},
  pages = {281--282},
  title = {{Leveraging Human Inputs in Interactive Machine Learning for Human Robot Interaction}},
  url = {http://dl.acm.org/citation.cfm?doid=3029798.3038385},
  year = {2017}
}

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