Project description
Robot autonomy and decision-making capabilities
Today’s robots are limited to controlled environments. Even advanced humanoids like Atlas rely on manual control and detailed workspace models, leaving little room for sensing. As a result, robots lack the agility, dexterity, autonomy, robustness and safety needed to operate alongside people in unpredictable real-world situations. The ERC-funded ARTIFACT project aims to develop a modular, learnable control architecture to enhance robot decision-making capabilities. By leveraging differentiable programming, robots will create interaction models from sensor data and physics principles. They will autonomously discover complex gestures and movements based on past experiences, learning to manage interactions and reason about their environment. The project will produce open-source software demonstrated in real-world scenarios that require fine dexterity and agility.
Objective
Todays robots are confined to tightly controlled environments: even the complex choreographies that the Atlas humanoid flawlessly executes heavily rely on handcrafted control strategies and detailed workspace models, with little place for sensing. To put it bluntly, robots are nowhere near the level of agility, dexterity, and even less so autonomy, robustness, and safety required for their deployment in the wild alongside people.
The tenet of ARTIFACT is that the key to an actual revolution will come from the algorithmic foundations of artificial motion intelligence, an AI challenged from the start to interact physically with dynamic environments and, ultimately, people. To do so, we will break away from the dichotomy between optimal control, where the role of perception is traditionally limited to an early state estimation stage, and reinforcement learning, where control policies are typically learned model-free with no guarantee to cope with the curse of dimensionality.
In ARTIFACT, we will devise a unified, structured, modular, and learnable control architecture for providing robots with advanced decision-making capabilities to solve complex tasks and face new interactions as they experience the world. It will leverage the notion of differentiable programming at all scales to enable robots to (i) capture models of their interactions directly from a sound combination of sensor data and first principles from physics, (ii) autonomously discover new complex gestures and movements leveraging their past experiences, and (iii) learn embodied representations to control their interactions finely and reason about the physical world. It will be implemented in open-source software and shown in real-world and challenging scenarios requiring fine dexterity and high agility. Altogether, these contributions will be the key enablers to enhance robot autonomy fundamentally, thus opening the age of ubiquitous robots at the service of mankind.
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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)
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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
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(opens in new window) ERC-2024-STG
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78153 Le Chesnay Cedex
France
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