Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Creating High-quality Paths for Motion Planning
International Journal of Robotics Research
Speeding up learning in real-time search via automatic state abstraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Manipulation planning with workspace goal regions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Path planning in 1000+ dimensions using a task-space Voronoi bias
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Search-based planning for a legged robot over rough terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Hierarchical A *: searching abstraction hierarchies efficiently
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
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We present a class of methods for optimal holonomic planning in high-dimensional spaces that automatically learns and leverages low-dimensional structure to efficiently find high-quality solutions. These methods are founded on the principle that problems possessing such structure are inherently simple to solve. This is demonstrated by presenting algorithms to solve these problems in time that scales with the dimension of a salient subspace, as opposed to the scaling with configuration-space dimension that would result from a naive approach. For generic problems possessing only approximate low-dimensional structure, we give iterative algorithms that are guaranteed convergence to local optima while making non-local path adjustments to escape poor local minima. We detail the theoretical underpinnings of these methods as well as give simulation and experimental results demonstrating the ability of our approach to efficiently find solutions of a quality exceeding that of known methods, and in problems of high dimensionality.