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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2017
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
80539 MUNCHEN
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.