  Robust environment perception is essential for decision-making on robots
operating in complex domains. Intelligent task execution requires principled
treatment of uncertainty sources in a robot's observation model. This is
important not only for low-level observations (e.g., accelerometer data), but
also for high-level observations such as semantic object labels. This paper
formalizes the concept of macro-observations in Decentralized Partially
Observable Semi-Markov Decision Processes (Dec-POSMDPs), allowing scalable
semantic-level multi-robot decision making. A hierarchical Bayesian approach is
used to model noise statistics of low-level classifier outputs, while
simultaneously allowing sharing of domain noise characteristics between
classes. Classification accuracy of the proposed macro-observation scheme,
called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed
existing methods. The macro-observation scheme is then integrated into a
Dec-POSMDP planner, with hardware experiments running onboard a team of dynamic
quadrotors in a challenging domain where noise-agnostic filtering fails. To the
best of our knowledge, this is the first demonstration of a real-time,
convolutional neural net-based classification framework running fully onboard a
team of quadrotors in a multi-robot decision-making domain.
