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?
DOI
10.1145/3029798.3038385
Publication Date
2017-03-10
Publication Title
Proceedings of the 2017 ACM/IEEE Human-Robot Interaction Conference
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Senft, E., Lemaignan, S., Baxter, P., & Belpaeme, T. (2017) 'Leveraging Human Inputs in Interactive Machine Learning for Human Robot Interaction', Proceedings of the 2017 ACM/IEEE Human-Robot Interaction Conference, . Available at: https://doi.org/10.1145/3029798.3038385
