Back to the Future for Consistency-Based Trajectory Tracking

Abstract

Given a model of a physical process and a sequence of com-mands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observ-able Markov decision processes with very large state spaces. The algorithm incrementally generates, rather than revises, an approximate belief state at any point by abstracting and sum-marizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial be-lief state when it remains consistent with observations and re-visit past assumptions about the process’s evolution when the belief state is ruled out. The system presented has been im-plemented and results on examples from the domain of space-craft control are presented.