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LEACON SIGNED

LEArning-CONtrol tight interaction: a novel approach to robust execution of mobile manipulation tasks

Total Cost €

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EC-Contrib. €

0

Partnership

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Project "LEACON" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITAET MUENCHEN 

Organization address
address: Arcisstrasse 21
city: MUENCHEN
postcode: 80333
website: www.tu-muenchen.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website https://leacondot.wordpress.com/
 Total cost 159˙460 €
 EC max contribution 159˙460 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-03-07   to  2018-03-06

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITAET MUENCHEN DE (MUENCHEN) coordinator 159˙460.00

Map

 Project objective

One of the main challenges of roboticists is to take robots out of the factories and let them enter into unstructured environments, such as houses, hospitals, small manufacturers and dangerous area. The objective of the project is to take a step towards the presence of robots in such environments. Currently, there are still important obstacles to the massive diffusion of advanced mobile manipulation systems in the fields described above. First of all, programming mobile manipulators with the classical methods is still too expensive and time-consuming due to intrinsic complexity of mobile manipulation tasks. A second limitation is that planning the robot motion completely off-line, as often happens in classical industrial applications, may likely bring to a failure of the assigned task, since a high degree of uncertainty is present and the environment can dynamically change. Such features may cause safety issues for humans potentially present in the workspace and for the external environment itself. In order to tackle these limitations, the LEACON project has the objective to develop a framework that: - allows robots to learn in a real world scenario manipulation skills from human demonstration -exploits multimodal perception (tactile, proximity, visual, force sensors) to increase the robustness to unforeseen events and safety when manipulation tasks are executed. To fulfill such objectives, a multidisciplinary approach that combines machine learning and perception-based control is proposed. The core of the proposed framework will provide two planning levels tightly connected: the high-level and low-level cognitive system. To show the effectiveness of the developed architecture, the main use cases will be constituted by a robot that performs picking, manipulation, and placing operations in a dynamic, unstructured environment in presence of humans in its workspace. At the end of the project, the developed software will be released as open source code.

 Publications

year authors and title journal last update
List of publications.
2017 Pietro Falco, Matteo Saveriano, Eka Gibran Hasany, Nicholas H. Kirk, Dongheui Lee
A Human Action Descriptor Based on Motion Coordination
published pages: 811-818, ISSN: 2377-3766, DOI: 10.1109/LRA.2017.2652494
IEEE Robotics and Automation Letters 2/2 2019-06-17
2017 M. Saveriano, Y. Yin, P. Falco, D. Lee
Data-Efficient Control Policy Search using Residual Dynamics Learning
published pages: , ISSN: , DOI:
International Conference on Intelligent Robots and Systems (IROS) 2019-06-17
2018 Pietro Falco, Matteo Saveriano, Dharmil Shah, Dongheui Lee
Representing human motion with FADE and U-FADE: an efficient frequency-domain approach
published pages: , ISSN: 0929-5593, DOI: 10.1007/s10514-018-9722-9
Autonomous Robots 2019-06-17
2018 Fanny Ficuciello, Pietro Falco, Sylvain Calinon
A Brief Survey on the Role of Dimensionality Reduction in Manipulation Learning and Control
published pages: 1-1, ISSN: 2377-3766, DOI: 10.1109/LRA.2018.2818933
IEEE Robotics and Automation Letters 2019-06-17
2018 Pietro Falco, Abdallah Attawia, Matteo Saveriano, Dongheui Lee
On Policy Learning Robust to Irreversible Events: an Application to Robotic In-Hand Manipulation
published pages: 1-1, ISSN: 2377-3766, DOI: 10.1109/LRA.2018.2800110
IEEE Robotics and Automation Letters 2019-06-17

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The information about "LEACON" are provided by the European Opendata Portal: CORDIS opendata.

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