Skip to main content
Go to the home page of the European Commission (opens in new window)
English English
CORDIS - EU research results
CORDIS

Reconstructing the Past: Artificial Intelligence and Robotics Meet Cultural Heritage

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Integration guidelines and plan 3 (opens in new window)

Describes the 3rd version of the guidelines and plans needed for a successful integration of the different components of the RePAIR system.

Integration guidelines and plan 1 (opens in new window)

Describes the 1st version of the guidelines and plans needed for a successful integration of the different components of the RePAIR system.

Integration guidelines and plan 2 (opens in new window)

Describes the 2nd version of the guidelines and plans needed for a successful integration of the different components of the RePAIR system.

Setup of integration and sharing repository (opens in new window)

Describes the setup of the repositories to be created to facilitate interaction during the development phase

Fragment recognition and pose estimation (opens in new window)

Describes the work done in WP2.2 where we shall develop matching algorithms to recognise and estimate the pose of the fragments, given possibly partial views and different sensor modalities.

Bi-manual motion planning (opens in new window)

Describes the work done in WP5.2 whew we will develop efficient motion planning techniques for the bi-manual robotic system.

Acquisition and 3D modelling of fragments (opens in new window)

Describes the work done in WP21 which will focus on deriving an accurate 3D model for each archaeological fragment together with a mapped texture so as to obtain appearance and geometrybased descriptors that will be used by the puzzlesolving and grasping modules

Hand pose selection for grasp planning using vision sensors (opens in new window)

Describes the work done in WP51 which aims to learn by explorationimitation the parameters of the visual servoing controllers for softhands

Adaptable bi-manual manipulation platform (opens in new window)

Describes the work done in WP4.2 which focuses on the development and control of a dedicated twin robot arm to execute the manipulation tasks of the fresco pieces.

Quality Management plan (opens in new window)

Sets the quality standards to be pursued in the whole project

Exploitation plan (including IP provisions) (opens in new window)

Describes the exploitation plan of the project including IP provisions

Manipulation platform requirements (opens in new window)

Describes the work done in WP41 and outlines the desired specifications of the robot and vision systems based on the requirements of the manipulation tasks with the fresco pieces

Style priors and their formal description for appearance prediction (opens in new window)

Describes the work done in WP31 where highlevel knowledge of fresco styles will be formalised in ways that allow its integration into the puzzle solving algorithm

View selection for visual perception tasks (opens in new window)

Describes the work done in WP5.3 which will develop optimisation methods to select the arm pose that provides the most informative visual input for a successful grasp.

End-effectors modules (opens in new window)

Describes the work done in WP 4.3 where we will leverage on soft robotic principles together with under-actuation to develop soft and adaptive end-effectors.

Strategic communication and dissemination plan (opens in new window)

Describes the strategic communication and dissemination plan of the project

Data Management Plan (opens in new window)

It provides indications as to the management of data in relation to the H2020 Open Research Data Pilot Data Management Plan might be updated during the project implementation

Publications

3DSGrasp: 3D Shape-Completion for Robotic Grasp (opens in new window)

Author(s): Seyed S. Mohammadi, Nuno F. Duarte, Dimitrios Dimou, Yiming Wang, Matteo Taiana, Pietro Morerio, Atabak Dehban, Plinio Moreno, Alexandre Bernardino, Alessio Del Bue, José Santos-Victor
Published in: 2023 IEEE International Conference on Robotics and Automation (ICRA), 2024, Page(s) 3815-3822
Publisher: IEEE
DOI: 10.1109/icra48891.2023.10160350

Learning Goal-Oriented Non-Prehensile Pushing in Cluttered Scenes (opens in new window)

Author(s): Nils Dengler, David Großklaus, Maren Bennewitz
Published in: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024, Page(s) 1116-1122
Publisher: IEEE
DOI: 10.1109/iros47612.2022.9981873

Relaxation labeling meets GANs: Solving jigsaw puzzles with missing borders (opens in new window)

Author(s): M. Khoroshiltseva, A. Traviglia, M. Pelillo and S. Vascon
Published in: Proceedings of the 21st International Conference on Image Analysis and Processing, 2022
Publisher: Springer
DOI: 10.1007/978-3-031-06433-3_3

Multi-phase relaxation labeling for square jigsaw puzzle solving (opens in new window)

Author(s): B. Vardi, M. Khoroshiltseva, A. Torcinovich, M. Pelillo, and O. Ben-Shahar
Published in: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging, and Computer Graphics: Theory and Applications, 2023
Publisher: SCITEPRESS
DOI: 10.5220/0011622800003417

GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image (opens in new window)

Author(s): D. Talon, A. Del Bue, S. James
Published in: Proceedings of IEEE International Conference on Image Processing, 2022
Publisher: IEEE
DOI: 10.1109/icip46576.2022.9897553

Searching for OpenAIRE data...

There was an error trying to search data from OpenAIRE

No results available

My booklet 0 0