PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments

Understand

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution.

• Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states.
• However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges.
• Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime.