Mixed observability MDPs for shared autonomy with uncertain human behaviour

Shared autonomy allows humans and AI operators to work towards a common goal. Typically, shared autonomy systems are modelled by combining a single model for human behaviour, and a model for the AI behaviour. In this paper, we attempt to provide a richer human model, which accounts for variation in performance due to factors that are not directly observable. Our shared autonomy system will maintain a belief over the unobservable factors, and update its belief as they make observations. The new belief is used to decide who should operate the shared autonomy system. We show that using our model with a richer human representation results in better performance than using a simplistic human model.