09:30 - 09:45 | Mon 1 Jun | Room T1 | MoA01.2
11:15 - 11:30 | Mon 1 Jun | Room T19 | MoB19.1
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.