Human-Centric Active Perception for Autonomous Observation
This work addresses the challenge of enabling robots to autonomously observe human activities, which is incremental as it builds on existing MDP methods for specific applications like space station monitoring.
The paper tackled the problem of autonomous human observation by formulating it as a multi-objective optimization using a novel Semi-MDP approach, and validated it in a simulated International Space Station environment with a NASA Astrobee robot, achieving effective activity tracking.
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.