Inferring finite automata with stochastic output functions and an application to map learning We assume that it is useful for a robot to construct a spatial representation of its environment for navigation purposes. In addition, we assume that robots, like people, make occasional errors in perceiving the spatial features of their environment. Typical perceptual errors include confusing two distinct locations or failing to identify the same location seen at different times. We are interested in the consequences of perceptual uncertainty in terms of the time and space required to learn a map with a given accuracy. We measure accuracy in terms of the probability that the robot correctly identifies a particular underlying spatial configuration. We derive considerable power by providing the robot with routines that allow it to identify landmarks on the basis of local features. We provide a mathematical model of the problem and algorithms that are guaranteed to learn the underlying spatial conliguration for a given class of environments with probability 1  xcexb4 in time polynomial in 1xcexb4 and some measure of the structural complexity of the environment and the robotu0027s ability to discern that structure. Our algorithms apply to a variety of environments that can be modeled as labeled graphs or deterministic finite automata.

Optimal Temporal Logic Planning for MultiRobot Systems in Uncertain Semantic Maps This paper addresses a multirobot motion planning problem in probabilistic maps obtained by semantic simultaneous localization and mapping (SLAM). The goal of the robots is to accomplish complex collaborative high level tasks captured by global temporal logic specifications in the presence of uncertainty in the workspace. Specifically, the robots operate in an unknown environment modeled as a semantic map determined by Gaussian distributions over landmark positions and arbitrary discrete distributions over landmark classes. We extend Linear Temporal Logic by including informationbased predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification. We propose a new highly scalable samplingbased approach that synthesizes paths that satisfy the assigned task specification while minimizing a userspecified motion cost function. Finally, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and supported by convergence rate bounds. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address largescale planning tasks.