C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Neural networks for pattern recognition
Neural networks for pattern recognition
Continuous case-based reasoning
Artificial Intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Artificial intelligence and mobile robots: case studies of successful robot systems
Artificial intelligence and mobile robots: case studies of successful robot systems
Map learning and high-speed navigation in RHINO
Artificial intelligence and mobile robots
Artificial intelligence and mobile robots
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
An Behavior-based Robotics
Robot Motion Planning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Explanation-Based Neural Network Learning for Robot Control
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Dynamic Programming
Acting Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation
Acting Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Does it help a robot navigate to call navigability an affordance?
Proceedings of the 2006 international conference on Towards affordance-based robot control
| Hi-index | 0.03 |
Most state-of-the-art navigation systems for autonomous service robots decompose navigation into global navigation planning and local (reactive) navigation. While the methods for navigation planning and local navigation themselves are well understood, the plan execution problem, the problem of how to generate and parameterize local navigation actions from a given navigation plan, is largely unsolved.This article describes how a robot can autonomously learn to execute navigation plans. We investigate how the robot can acquire causal models of the actions executable by the local navigation system and we develop a decision theoretic action selection function which uses the models learned to execute a given navigation plan. Finally, we show, both in simulation and on a RWI B21 mobile robot, that the learned action selection function improves the robot's navigation performance compared to standard plan execution techniques.