Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Tree based discretization for continuous state space reinforcement learning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Reinforcement Learning in Continuous Time and Space
Neural Computation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Indoor Navigation for a Humanoid Robot Using a View Sequence
International Journal of Robotics Research
Which landmark is useful?: learning selection policies for navigation in unknown environments
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A visual odometry framework robust to motion blur
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
icLQG: combining local and global optimization for control in information space
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning efficient policies for vision-based navigation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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In this article, we present a novel approach to learning efficient navigation policies for mobile robots that use visual features for localization. As fast movements of a mobile robot typically introduce inherent motion blur in the acquired images, the uncertainty of the robot about its pose increases in such situations. As a result, it cannot be ensured anymore that a navigation task can be executed efficiently since the robot's pose estimate might not correspond to its true location. We present a reinforcement learning approach to determine a navigation policy to reach the destination reliably and, at the same time, as fast as possible. Using our technique, the robot learns to trade off velocity against localization accuracy and implicitly takes the impact of motion blur on observations into account. We furthermore developed a method to compress the learned policy via a clustering approach. In this way, the size of the policy representation is significantly reduced, which is especially desirable in the context of memory-constrained systems. Extensive simulated and real-world experiments carried out with two different robots demonstrate that our learned policy significantly outperforms policies using a constant velocity and more advanced heuristics. We furthermore show that the policy is generally applicable to different indoor and outdoor scenarios with varying landmark densities as well as to navigation tasks of different complexity.