Flexible POMDP Framework for Human-Robot Cooperation in Escort Tasks

We describe a novel method for ensuring cooperation between human and robot. First, we present a flexible and hierarchical framework based on POMDPs. Second, we introduce a set of cooperative states within the state-space of the POMDP. Third, for ensuring an efficient scalability, the framework partitions the overall task into independent planning modules. Lastly, for a robust execution of the POMDP policies we use Petri Net Plans, which have already been used to execute MDP policies. To this end, we describe how to convert a POMDP policy into an executable Petri Net Plan. We implement our approach and develop experiments on simulation and on a real robot in an escorting task where the robot guides a customer to the desired place in a public space.