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SAHR SIGNED

Skill Acquisition in Humans and Robots

Total Cost €

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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.

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

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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