| Title: |
Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning |
| Authors: |
Labbé, Yann; Zagoruyko, Sergey; Kalevatykh, Igor; Laptev, Ivan; Carpentier, Justin; Aubry, Mathieu; Sivic, Josef |
| Contributors: |
Models of visual object recognition and scene understanding (WILLOW); Département d'informatique - ENS Paris (DI-ENS); École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria); Université Paris Sciences et Lettres (PSL); Laboratoire d'Informatique Gaspard-Monge (LIGM); École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel; This work was partially supported by the DGA RAPID projects DRAAF and TABASCO, the MSR-Inria joint lab, the Louis Vuitton - ENS Chair on Artificial Intelligence, the HPC resources from GENCI-IDRIS (Grant 011011181), the ERC grant LEAP (No. 336845), the CIFAR Learning in Machines\&Brains program, the European Regional Development Fund under the project IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000468) and the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute).; ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019) |
| Source: |
ISSN: 2377-3766 ; IEEE Robotics and Automation Letters ; https://hal.science/hal-02108930 ; IEEE Robotics and Automation Letters, 2020, 5 (2), pp.3715-3722. ⟨10.1109/LRA.2020.2980984⟩. |
| Publisher Information: |
HAL CCSD; IEEE |
| Publication Year: |
2020 |
| Collection: |
Université de Rennes 1: Publications scientifiques (HAL) |
| Subject Terms: |
task planning; perception for grasping and manipulation; AI-based methods; computer vision; robotics; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] |
| Description: |
International audience ; We address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying on visual inputs coming from an RGB camera. To do so, we introduce a complete pipeline relying on two key contributions. First, we introduce an efficient and scalable rearrangement planning method, based on a Monte-Carlo Tree Search exploration strategy. We demonstrate that because of its good trade-off between exploration and exploitation our method (i) scales well with the number of objects while (ii) finding solutions which require a smaller number of moves compared to the other state-of-the-art approaches. Note that on the contrary to many approaches, we do not require any buffer space to be available. Second, to precisely localize movable objects in the scene, we develop an integrated approach for robust multi-object workspace state estimation from a single uncalibrated RGB camera using a deep neural network trained only with synthetic data. We validate our multi-object visually guided manipulation pipeline with several experiments on a real UR-5 robotic arm by solving various rearrangement planning instances, requiring only 60 ms to compute the plan to rearrange 25 objects. In addition, we show that our system is insensitive to camera movements and can successfully recover from external perturbations. Supplementary video, source code and pre-trained models are available at https://ylabbe.github.io/rearrangement-planning/. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/arxiv/1904.10348; ARXIV: 1904.10348 |
| DOI: |
10.1109/LRA.2020.2980984 |
| Availability: |
https://hal.science/hal-02108930; https://hal.science/hal-02108930v1/document; https://hal.science/hal-02108930v1/file/1904.10348.pdf; https://doi.org/10.1109/LRA.2020.2980984 |
| Rights: |
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.5602D0F9 |
| Database: |
BASE |