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About this paper
Rayen Tan, Rohan Ghuge, Viswanath Nagarajan
The authors develop limited-round informative path planning for robots that learn hidden scenarios while traveling. Each round follows a planned tour before observations update the next route. They prove approximation guarantees for general planning and hypothesis identification. In ten simulated road-network instances, two rounds averaged within 50% of fully adaptive cost and planned about 15 times faster, though individual costs varied.
Summary by alphaXiv.
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