Victor Hwang

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—Motion planning in high dimensional state spaces, such as for mobile manipulation, is a challenging problem. Constrained manipulation, e.g. opening articulated objects like doors or drawers, is also hard since sampling states on the constrained manifold is expensive. Further, planning for such tasks requires a combination of planning in free space for(More)
The performance of heuristic search based planners depends heavily on the quality of the heuristic function used to focus the search. These algorithms work fast and generate high-quality solutions, even for high-dimensional problems, as long as they are given a well-designed heuristic function. On the other hand, their performance can degrade considerably(More)
— Many robot applications involve lifelong planning in relatively static environments e.g. assembling objects or sorting mail in an office building. In these types of scenarios, the robot performs many tasks over a long period of time. Thus, the time required for computing a motion plan becomes a significant concern, prompting the need for a fast and(More)
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