Getting the shot: Socially-aware viewpoints for autonomously observing tasks

In this work, we present an algorithm for autonomously determining the appropriate location from which to observe a human or robot agent (actor) while it completes a task in dynamic environments. We develop theory for selecting such a location using forward physical simulation of randomly-selected candidate viewpoints. The simulated points provide obstacle avoidance, and by incorporating a modified version of the Social Force Model, candidate viewpoints adjust themselves so that they do not encroach on the actor's personal space and/or safety region. The best observer position is chosen from these candidates to provide the most complete view of the task volume, taking into account the occlusion caused by the actor itself. We show that our algorithm works under a variety of task volume configurations, actor types (human and robot), and environmental constraints. Finally, the paper shows the results of hardware deployment on a two-robot system-one observer, and one actor. The paper concludes by examining the social impacts of deploying autonomous observation algorithms on real-world systems.

I. Introduction

As autonomous physical systems become more widespread in society, the need arises for robots that are both functional and socially aware. Many functional robots, such as unmanned aerial vehicles (UAVs) used for sport filming, have limited “follow” capabilities, but may not include robust obstacle avoidance, or may not respect humans' personal space. This can cause issues when operating in confined or crowded spaces, or may simply make the filming subject uncomfortable because of the UAV's proximity. On the other end of the spectrum lie socially aware robots, which have arisen from research in human-robot interaction. Robots such as Jibo [1] and Leonardo [2] may be “human-compatible” and instill human confidence, and can perform many software-based actions, such as face recognition, but do not perform many useful physical tasks.