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83
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approxi ..."
Abstract

Cited by 606 (83 self)
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Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 447 (52 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
Efficient Exploration In Reinforcement Learning
, 1992
"... Exploration plays a fundamental role in any active learning system. This study evaluates the role of exploration in active learning and describes several local techniques for exploration in finite, discrete domains, embedded in a reinforcement learning framework (delayed reinforcement). This paper d ..."
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Cited by 121 (4 self)
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Exploration plays a fundamental role in any active learning system. This study evaluates the role of exploration in active learning and describes several local techniques for exploration in finite, discrete domains, embedded in a reinforcement learning framework (delayed reinforcement). This paper distinguishes between two families of exploration schemes: undirected and directed exploration. While the former family is closely related to random walk exploration, directed exploration techniques memorize explorationspecific knowledge which is used for guiding the exploration search. In many finite deterministic domains, any learning technique based on undirected exploration is inefficient in terms of learning time, i.e. learning time is expected to scale exponentially with the size of the state space (Whitehead, 1991b) . We prove that for all these domains, reinforcement learning using a directed technique can always be performed in polynomial time, demonstrating the important role of e...
Particle Filters for Mobile Robot Localization
, 2001
"... This article describes a family of methods, known as Monte Carlo localization (MCL) (Dellaert at al. 1999b, Fox et al. 1999b). The MCL algorithm is a particle filter combined with probabilistic models of robot perception and motion. Building on this, we will describe a variation of MCL which uses a ..."
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Cited by 93 (18 self)
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This article describes a family of methods, known as Monte Carlo localization (MCL) (Dellaert at al. 1999b, Fox et al. 1999b). The MCL algorithm is a particle filter combined with probabilistic models of robot perception and motion. Building on this, we will describe a variation of MCL which uses a different proposal distribution (a mixture distribution) that facilitates fast recovery from global localization failures. As we will see, this proposal distribution has a range of advantages over that used in standard MCL, but it comes at the price that it is more difficult to implement, and it requires an algorithm for sampling poses from sensor measurements, which might be difficult to obtain. Finally, we will present an extension of MCL to cooperative multirobot localization of robots that can perceive each other during localization. All these approaches have been tested thoroughly in practice. Experimental results are provided to demonstrate their relative strengths and weaknesses in practical robot applications.
Memory Approaches To Reinforcement Learning In NonMarkovian Domains
, 1992
"... Reinforcement learning is a type of unsupervised learning for sequential decision making. Qlearning is probably the bestunderstood reinforcement learning algorithm. In Qlearning, the agent learns a mapping from states and actions to their utilities. An important assumption of Qlearning is the Ma ..."
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Cited by 61 (3 self)
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Reinforcement learning is a type of unsupervised learning for sequential decision making. Qlearning is probably the bestunderstood reinforcement learning algorithm. In Qlearning, the agent learns a mapping from states and actions to their utilities. An important assumption of Qlearning is the Markovian environment assumption, meaning that any information needed to determine the optimal actions is reflected in the agent's state representation. Consider an agent whose state representation is based solely on its immediate perceptual sensations. When its sensors are not able to make essential distinctions among world states, the Markov assumption is violated, causing a problem called perceptual aliasing. For example, when facing a closed box, an agent based on its current visual sensation cannot act optimally if the optimal action depends on the contents of the box. There are two basic approaches to addressing this problem using more sensors or using history to figure out the curren...
Issues in Using Function Approximation for Reinforcement Learning
 IN PROCEEDINGS OF THE FOURTH CONNECTIONIST MODELS SUMMER SCHOOL
, 1993
"... ..."
Planning by Incremental Dynamic Programming
 In Proceedings of the Eighth International Workshop on Machine Learning
, 1991
"... This paper presents the basic results and ideas of dynamic programming as they relate most directly to the concerns of planning in AI. These form the theoretical basis for the incremental planning methods used in the integrated architecture Dyna. These incremental planning methods are based on conti ..."
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Cited by 60 (2 self)
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This paper presents the basic results and ideas of dynamic programming as they relate most directly to the concerns of planning in AI. These form the theoretical basis for the incremental planning methods used in the integrated architecture Dyna. These incremental planning methods are based on continually updating an evaluation function and the situationaction mapping of a reactive system. Actions are generated by the reactive system and thus involve minimal delay, while the incremental planning process guarantees that the actions and evaluation function will eventually be optimal  no matter how extensive a search is required. These methods are well suited to stochastic tasks and to tasks in which a complete and accurate model is not available. For tasks too large to implement the situationaction mapping as a table, supervisedlearning methods must be used, and their capabilities remain a significant limitation of the approach.
Monte Carlo localization with mixture proposal distribution
 in Proc. 17th National Conf. on Artificial Intelligence (AAAI2000). AAAI Press/The
, 2000
"... Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a limitation of MCL which is counterintuitive, namely that better sensors can yield worse results. An analysis of this pro ..."
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Cited by 57 (10 self)
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Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a limitation of MCL which is counterintuitive, namely that better sensors can yield worse results. An analysis of this problem leads to the formulation of a new proposal distribution for the Monte Carlo sampling step. Extensive experimental results with physical robots suggest that the new algorithm is significantly more robust and accurate than plain MCL. Obviously, these results transcend beyond mobile robot localization and apply to a range of particle filter applications.