Artificial Intelligence Review - Special issue on lazy learning
Nonsmooth analysis and control theory
Nonsmooth analysis and control theory
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Global likelihood optimization via the cross-entropy method with an application to mixture models
WSC '04 Proceedings of the 36th conference on Winter simulation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Planning Algorithms
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
A Model Reference Adaptive Search Method for Global Optimization
Operations Research
A Study on the Cross-Entropy Method for Rare-Event Probability Estimation
INFORMS Journal on Computing
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A Generalized Path Integral Control Approach to Reinforcement Learning
The Journal of Machine Learning Research
On the performance of the cross-entropy method
Winter Simulation Conference
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
Maneuver-based motion planning for nonlinear systems with symmetries
IEEE Transactions on Robotics
Convergence properties of the cross-entropy method for discrete optimization
Operations Research Letters
Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning
Robotics and Autonomous Systems
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This paper is concerned with motion planning for non-linear robotic systems operating in constrained environments. A method for computing high-quality trajectories is proposed building upon recent developments in sampling-based motion planning and stochastic optimization. The idea is to equip sampling-based methods with a probabilistic model that serves as a sampling distribution and to incrementally update the model during planning using data collected by the algorithm. At the core of the approach lies the cross-entropy method for the estimation of rare-event probabilities. The cross-entropy method is combined with recent optimal motion planning methods such as the rapidly exploring random trees (RRT*) in order to handle complex environments. The main goal is to provide a framework for consistent adaptive sampling that correlates the spatial structure of trajectories and their computed costs in order to improve the performance of existing planning methods.