  Human-robot collaboration (HRC) has emerged as a hot research area at the
intersection of control, robotics, and psychology in recent years. It is of
critical importance to obtain an expressive but meanwhile tractable model for
human beings in HRC. In this paper, we propose a model called Vector
Autoregressive POMDP (VAR-POMDP) model which is an extension of the traditional
POMDP model by considering the correlation among observations. The VAR-POMDP
model is more powerful in the expressiveness of features than the traditional
continuous observation POMDP since the traditional one is a special case of the
VAR-POMDP model. Meanwhile, the proposed VAR-POMDP model is also tractable, as
we show that it can be effectively learned from data and we can extend
point-based value iteration (PBVI) to VAR-POMDP planning. Particularly, in this
paper, we propose to use the Bayesian non-parametric learning to decide
potential human states and learn a VAR-POMDP model using data collected from
human demonstrations. Then, we consider planning with respect to PCTL which is
widely used as safety and reachability requirement in robotics. Finally, the
advantage of using the proposed model for HRC is validated by experimental
results using data collected from a driver-assistance test-bed.
