Project description
Making robots learn quickly
Learning from experience is a basic human trait that allows us to become very dexterous and successful in a variety of tasks. Enabling autonomous robots to learn effectively from experience would allow them to solve new and challenging tasks, and the exploitation of their specific capabilities could make them ubiquitous assistants to humans. Currently, model-free reinforcement learning methods are applied, but these require a huge number of interactions with the real world. With all this in mind, the ERC-funded REAL-RL project will explore a model-based approach in which interactions with the world are learned from experience and captured in a learned model. The latter can be used for mental simulation, thereby decreasing the required amount of real-world interactions. The project will develop generic learning methods that can be used to control any robot with legs, arms or other morphologies.
Objective
REAL-RL proposes a path to autonomous robots that learn from experience. By learning to solve new and challenging tasks and exploiting their specific capabilities, they could become ubiquitous assistants to humans in an uncountable number of tasks. Current control strategies for robots are developed only for particular tasks and are not versatile. To ensure their functioning, it is necessary to have highly accurate physical models that precisely match all the essential aspects of the real world. REAL-RL follows a different path: a learning approach to robot control. The dominant direction in the field uses model-free reinforcement learning methods that need an incredible number of interactions with the world – often prohibitive for real robots. As a bypass, simulations are used but require detailed knowledge of all possible situations that the robot might encounter. These problems are circumvented in REAL-RL by proposing a model-based approach. Models of the interaction with the world are learned from experience and will be used to plan and adapt behavior on the fly. This approach promises to be much more data-efficient and allows to transfer of valuable experience between tasks. Fundamental challenges in model-learning, safety-aware exploration and planning, and higher-order reasoning are identified and presented here with concrete novel solution ideas, such as a causal inductive bias for deep dynamics models, risk-aware real-time general trajectory optimization, and differentiable discrete planning. Critical stepping stones, such as probabilistic models and fast trajectory planning, have just been developed by the community, and the applicant. By aiming at a generic learning method that can be used to control any robot – rigid or soft – with legs, arms, or other end-effectors for manipulation and locomotion tasks, and make them improve with experience, the proposal develops a solid basis for future robotic applications.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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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.
HORIZON-ERC - HORIZON ERC Grants
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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) ERC-2021-COG
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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.
72074 Tuebingen
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.