Tidiness Score-Guided Monte Carlo Tree Search for Visual Tabletop Rearrangement

In this letter, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and...

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Vydané v:	IEEE robotics and automation letters Ročník 10; číslo 10; s. 10090 - 10097
Hlavní autori:	Kee, Hogun, Oh, Wooseok, Kang, Minjae, Ahn, Hyemin, Oh, Songhwai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Bathrooms Configurations data sets for robot learning Datasets deep learning methods Discriminators Feature extraction Government Manipulation planning Monte Carlo methods Monte Carlo simulation Planning Robot learning Search problems Searching Semantics Training Trajectory Visualization
ISSN:	2377-3766, 2377-3766
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