The ability to obtain accurate observations while navigating in uncertain environments is a difficult challenge in deploying robots. Robots have relied heavily on human supervisors who are always available to provide additional observations to reduce uncertainty. We are instead interested in taking advantage of humans who are already in the environment to receive observations. The challenge is in modeling these humans' availability and higher costs of interruption to determine when to query them during navigation. In this work, we introduce a Human Observation Provider POMDP framework (HOP-POMDP), and contribute new algorithms for planning and executing with HOP-POMDPs that account for the differences between humans and other probabilistic sensors that provide observations. We compare optimal HOP-POMDP policies that plan for needing humans' observations with oracle POMDP policies that do not take human costs and availability into account. We show in benchmark tests and real-world environments that the oracle policies match the optimal HOP-POMDP policy 60% of the time, and can be used in cases when humans are likely to be available on the shortest paths. However, the HOP-POMDP policies receive higher rewards in general as they take into account the possibility that a human may be unavailable. HOP-POMDP policies only need to be computed once prior to the deployment of the robot, so it is feasible to precompute and use in practice.
['Stephanie Rosenthal', 'Manuela M. Veloso']
Modeling humans as observation providers using POMDPs