Opendata, web and dolomites


Skill Acquisition in Humans and Robots

Total Cost €


EC-Contrib. €






 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.

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

Project "SAHR" data sheet

The following table provides information about the project.


Organization address
address: BATIMENT CE 3316 STATION 1
postcode: 1015

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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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.


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.


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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