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SAHR SIGNED

Skill Acquisition in Humans and Robots

Total Cost €

0

EC-Contrib. €

0

Partnership

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 SAHR project word cloud

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

capacity    robots    unexpected    reduce    vehicles    life    successes    appliances    strategies    bimanual    stages    amounts    laws    benefited    acquisition    controllers    constraints    leaps    sensory    appropriately    vast    reactivity    failures    ml    time    society    noise    ds    informs    skills    dexterous    ways    exceed    daily    meet    powerful    live    arm    demonstrations    precision    fast    solving    longitudinal    combine    environmental    robotic    coordinated    learning    synergies    doors    overcome    little    retrievable    plan    follows    computation    paced    date    environment    made    matching    craftsmanship    variables    react    dimensional    largely    dynamical    constrained    autonomous    received    endeavour    speed    feasible    platforms    engaged    skill    motor    slow    inform    machine    industrial    opening    though    line    run    robotics    data    explored    conduct    decades    robot    adapt    humans    planning    optimization    immediately    rehabilitation   

Project "SAHR" data sheet

The following table provides information about the project.

Coordinator
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE 

Organization address
address: BATIMENT CE 3316 STATION 1
city: LAUSANNE
postcode: 1015
website: www.epfl.ch

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 Switzerland [CH]
 Total cost 2˙492˙036 €
 EC max contribution 2˙492˙036 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-ADG
 Funding Scheme ERC-ADG
 Starting year 2017
 Duration (year-month-day) from 2017-10-01   to  2022-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE CH (LAUSANNE) coordinator 2˙492˙036.00

Map

 Project objective

Society is rapidly opening its doors to robots in our daily life with autonomous vehicles, rehabilitation devices and autonomous appliances. These robots will face unexpected changes in their environment, to which they will have to react immediately and appropriately. Even though robots exceed largely humans’ precision and speed of computation, they are far from matching humans’ capacity to adapt rapidly to unexpected changes. In the past decades, robotics has made leaps forward in the design of increasingly complex robotic platforms to meet these challenges. In this endeavour, it has benefited from advances in optimization for solving high-dimensional constrained problems and in machine learning (ML) to analyse vast amounts of data. These methods are powerful for planning in slow-paced tasks and when the environment is known. This project addresses a growing need for methods that show fast and on-line reactivity. We design controllers that can plan at run time and adapt to new environmental constraints. We offer a novel approach to robot learning that follows stages of skill acquisition in humans. To inform modelling, we conduct a longitudinal study of the acquisition of dexterous bimanual skills in craftsmanship. We study how humans exploit task uncertainty to overcome their sensory-motor noise, and how humans learn bimanual synergies to reduce the control variables. This study informs the design of novel learning strategies for robots that exploit failures as much as successes. We combine planning and ML to learn feasible control laws, retrievable at run time with no need for further optimization. We exploit properties of dynamical systems (DS), which have received little attention in robot control, and use ML to identify characteristics of DS, in ways that were not explored to date. The approach is assessed in live demonstrations of coordinated adaptation of a multi-arm/hand robotic system engaged in a fast-paced industrial task, in the presence of humans.

 Deliverables

List of deliverables.
Data Management Plan Open Research Data Pilot 2019-05-30 15:19:05

Take a look to the deliverables list in detail:  detailed list of SAHR deliverables.

 Publications

year authors and title journal last update
List of publications.
2019 Aude Billard
Trends and challenges in robot manipulation
published pages: , ISSN: 1095-9203, DOI:
Science 2019-08-05
2018 Yao, K., Fichera, B., Haget, A., Lauzana, I., and Billard, A.
Integrating Multisensory Information for Modeling Human Dexterous Bimanual Manipulation Skills.
published pages: , ISSN: , DOI:
\"Workhop Proceedings \"\"The Intelligence of Touch\"\"\" 2019-08-05
2019 Yao, K., Haget, A., and Billard, A
Towards understanding of human kinematic coordination patterns in bimanual fine manipulation tasks
published pages: , ISSN: , DOI:
PMC XII conference 2019 digital abstract book 2019-08-05

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