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Xavier: a robot navigation architecture based on partially observable Markov decision process models
Sven Koenig,Reid Simmons +1 more
- pp 91-122
Citations
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Journal ArticleDOI
Recent Advances in Hierarchical Reinforcement Learning
TL;DR: This work reviews several approaches to temporal abstraction and hierarchical organization that machine learning researchers have recently developed and discusses extensions of these ideas to concurrent activities, multiagent coordination, and hierarchical memory for addressing partial observability.
Journal ArticleDOI
Markov localization for mobile robots in dynamic environments
TL;DR: A version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments, and includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time.
Journal ArticleDOI
The spatial semantic hierarchy
TL;DR: The assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems are described and evidence is presented from several partial implementations of the ssh on simulated and physical robots.
Journal ArticleDOI
Active global localization for a mobile robot using multiple hypothesis tracking
Patric Jensfelt,S. Kristensen +1 more
TL;DR: The method uses multi-hypothesis Kalman filter based pose tracking combined with a probabilistic formulation of hypothesis correctness to generate and track Gaussian pose hypotheses online and generates movement commands for the platform to enhance the gathering of information for the pose estimation process.
Proceedings ArticleDOI
Supervised Learning of Places from Range Data using AdaBoost
TL;DR: This paper uses AdaBoost, a supervised learning algorithm, to train a set of classifiers for place recognition based on laser range data and describes how this approach can be applied to distinguish between rooms, corridors, doorways, and hallways.
References
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Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
TL;DR: The upper bound is obtained for a specific probabilistic nonsequential decoding algorithm which is shown to be asymptotically optimum for rates above R_{0} and whose performance bears certain similarities to that of sequential decoding algorithms.
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An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
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Using occupancy grids for mobile robot perception and navigation
TL;DR: An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid, a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice is reviewed.
Journal ArticleDOI
The Complexity of Markov Decision Processes
TL;DR: All three variants of the classical problem of optimal policy computation in Markov decision processes, finite horizon, infinite horizon discounted, and infinite horizon average cost are shown to be complete for P, and therefore most likely cannot be solved by highly parallel algorithms.