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ROBOTGENSKILL SIGNED

Generalizing human-demonstrated robot skills

Total Cost €

EC-Contrib. €

Partnership

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ROBOTGENSKILL project word cloud

Explore the words cloud of the ROBOTGENSKILL project. It provides you a very rough idea of what is the project "ROBOTGENSKILL" about.

model added close skills lot perform demonstrating factory motions follows effort variability free settings robots deploy space constraints situation consisting human robot intuitive context demonstration neighbouring objects physical rigid progress representation situations generalize fundamental innovative humans additional invariant hence operators industrial recognize generalization cleaning leverage future preserving complying intention home view similarity easily maintained conversion drastically optimal skill breakthroughs multitude forces manipulation imposed manipulated assembly solving biomechanics interaction motion people nature demonstrated contact react training decrease floor methodology data demonstrations

Project "ROBOTGENSKILL" data sheet

The following table provides information about the project.

Coordinator	KATHOLIEKE UNIVERSITEIT LEUVEN Organization address address: OUDE MARKT 13 city: LEUVEN postcode: 3000 website: www.kuleuven.be 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	Belgium [BE]
Total cost	2˙494˙971 €
EC max contribution	2˙494˙971 € (100%)
Programme	1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
Code Call	ERC-2017-ADG
Funding Scheme	ERC-ADG
Starting year	2018
Duration (year-month-day)	from 2018-10-01 to 2023-09-30

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	KATHOLIEKE UNIVERSITEIT LEUVEN KATHOLIEKE UNIVERSITEIT LEUVEN Organization address address: OUDE MARKT 13 city: LEUVEN postcode: 3000 website: www.kuleuven.be contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	BE (LEUVEN)	coordinator	2˙494˙971.00

Map

Project objective

Future robots are expected to perform a multitude of complex tasks with high variability, in close collaboration or even physical contact with humans, and in industrial as well as in non-industrial settings. Both human-robot interaction and task variability are major challenges. A lot of progress is needed so that: (1) robots recognize the intention of the human and react with human-like motions; (2) robot end-users, such as operators on the factory floor or people at home, are able to deploy robots for new tasks or new situations in an intuitive way, for example by just demonstrating the task to the robot.

The fundamental challenge addressed in this proposal is: how can a robot generalize a skill that has been demonstrated in a particular situation and apply it to new situations? This project focuses on skills involving rigid objects manipulated by a robot or a human and follows a model-based approach consisting of: (1) conversion of the demonstrated data to an innovative invariant representation of motion and interaction forces; (2) generalization of this representation to a new situation by solving an optimal control problem in which similarity with the invariant representation is maintained while complying with the constraints imposed by the new context. Additional knowledge about the task can be added in the constraints. Major breakthroughs are that the required number of demonstrations and hence the training effort decrease drastically, similarity with the demonstration is maintained in view of preserving the human-like nature, and task knowledge is easily included.

The methodology is applied to program robot skills involving motion in free space (e.g. human-robot hand over tasks) as well as advanced manipulation skills involving contact (e.g. assembly, cleaning), aiming at impact in industrial and non-industrial settings.

Application of the invariant motion representation in the neighbouring field of biomechanics will further leverage impact.

Publications

List of publications.
year	authors and title	journal	last update
2019	Maxim Vochten, Tinne De Laet, Joris De Schutter Generalizing demonstrated motion trajectories using coordinate-free shape descriptors published pages: 103291, ISSN: 0921-8890, DOI: 10.1016/j.robot.2019.103291	Robotics and Autonomous Systems 122	2019-10-16
2019	Maxim Vochten, Tinne De Laet, Joris De Schutter Improving Generalization in Trajectory Learning through Coordinate-free Shape Descriptors published pages: , ISSN: , DOI:	2019 IEEE International Conference on Robotics and Automation, LINCH Workshop May 24, 2019	2019-10-16

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