Opendata, web and dolomites

DESlRE SIGNED

Data-Efficient Scalable Reinforcement Learning for Practical Robotic Environments

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 DESlRE project word cloud

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

notably    adoption    robotics    recurrent    alphago    dynamics    works    record    exploration    self    networks    representations    environment    motivation    group    william    infogan    unsuitable    model    received    artificial    theory    machine    regimes    afford    cliff    single    predictive    continue    sensorimotor    unsafe    few    rl    robots    effort    intrinsic    breakthroughs    publishing    postdoctoral    models    autonomous    mature    rely    dimensions    power    data    conversely    he    amounts    spaces    code    host    industry    ai    explore    lpzrobots    track    thrive    disentangled    pilco    embodied    collecting    small    running    hager    minutes    tackle    frameworks    led    dimensional    world    representation    leverage    learning    previously    efficiency    online    methodology    practical    extensive    gain    plan    algorithms    martius    optimization    internal    ph    dr    pillars    trials    combine    researcher    bayesian    georg    interaction   

Project "DESlRE" data sheet

The following table provides information about the project.

Coordinator
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV 

Organization address
address: HOFGARTENSTRASSE 8
city: Munich
postcode: 80539
website: www.mpg.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]
 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-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2020-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV DE (Munich) coordinator 159˙460.00

Map

 Project objective

The robotics industry is in the process of greater adoption of machine learning. Recent reinforcement learning (RL) and AI breakthroughs, such as AlphaGo, rely on collecting large amounts of data. Such methods are unsuitable for real robots which often can only afford a few trials. Moreover, some states are unsafe to explore, e.g. running over a cliff. Conversely, works such as PILCO combine Bayesian models with model-based RL to improve data efficiency. Those frameworks typically thrive in small data regimes. The goal of this project is to develop RL algorithms that scale to high dimensions while learning with less data. The main pillars of our methodology are RL, recurrent networks, Bayesian methods, embodied exploration, and optimization. To tackle the data efficiency, we adopt model-based RL approaches. We plan to combine representation learning and dynamics in a single model, leading to high predictive power and low-dimensional internal state spaces. Notably, we use methods that can learn disentangled representations, e.g. infoGAN. In practical robots, effective exploration is a real problem in current approaches. We want to leverage recent works in embodied exploration by the host group which allows various real-world robots to explore their capabilities in minutes of interaction. I received my Ph.D. for work in optimization with Dr. William Hager. I also conducted postdoctoral research in machine learning. The Autonomous Learning group is led by Dr. Georg Martius, who has previously studied artificial intrinsic motivation, the self-organized exploration of sensorimotor coordination via information theory, and internal model learning. He also developed the robotics environment LPZRobots. I will gain extensive experience in practical robotics, embodied exploration, and information theory through the collaboration and mature as an advanced AI researcher. Both Dr. Martius and I have a track record of publishing code online. We will continue this effort.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "DESLRE" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.1.3.2.)

BirthControlEnvirons (2019)

Contraception meets the environment: everyday contraceptive practices, politics, and futures in a toxic age

Read More  

STIMOS (2019)

Stimulation of Multiple Organoids Simultaneously

Read More  

VINCI (2020)

The Value of Information and Choice to Improve Control.

Read More