Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning

Brian Ichter
Brian Ichter
Corey Lynch
Corey Lynch
Cited by: 0|Bibtex|Views21
Other Links: arxiv.org

Abstract:

Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planning algorithms, such as rapidly-exploring random trees, are capable of efficiently exploring large state spaces and computing long-horizon, sequential plans. H...More

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