Towards Cooperative Bayesian Human-Robot Perception: Theory, Experiments, Opportunities

Abstract

Robust integration of robotic and human perception abilities can greatly enhance the execution of complex information-driven tasks like search and rescue. Our goal is to formally characterize and combine diverse information streams obtained from multiple autonomous robots and humans within a unified probabilistic framework that naturally supports autonomous perception, human situational awareness, and cooperative human-robot task execution under stochastic uncertainties. This approach requires well-designed human-robot interfaces, flexible and accurate probabilistic models for exploiting “human sensor ” data, and sophisticated Bayesian inference methods for efficient learning and online dynamic state estimation. We review some of recent theoretical developments and insights from experiments using real human-robot teams, and discuss some open challenges for future research.