Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.
Sample-Based Planning with Volumes in Configuration Space
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abstract
A simple sample-based planning method is presented which approximates connected regions of free space with volumes in Configuration space instead of points. The algorithm produces very sparse trees compared to point-based planning approaches, yet it maintains probabilistic completeness guarantees. The planner is shown to improve performance on a variety of planning problems, by focusing sampling on more challenging regions of a planning problem, including collision boundary areas such as narrow passages.
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Neural Configuration-Space Barriers for Manipulation Planning and Control
Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.