  As robot autonomy improves, robots are increasingly being considered in the
role of autonomous observation systems -- free-flying cameras capable of
actively tracking human activity within some predefined area of interest. In
this work, we formulate the autonomous observation problem through
multi-objective optimization, presenting a novel Semi-MDP formulation of the
autonomous human observation problem that maximizes observation rewards while
accounting for both human- and robot-centric costs. We demonstrate that the
problem can be solved with both scalarization-based Multi-Objective MDP methods
and Constrained MDP methods, and discuss the relative benefits of each
approach. We validate our work on activity tracking using a NASA Astrobee robot
operating within a simulated International Space Station environment.
