Project description
Scaling up robot skill-learning capabilities
Significant advancements in AI, robotics, and digital technologies have led to breakthroughs in several sectors, especially automation. Unfortunately, both AI and robotics still struggle to engage with diverse objects, complex scenarios, and environments. There are also limitations in their capacity to manipulate objects of varying properties and geometries. Following intricate instruction manuals is also a challenge. With this in mind, the ERC-funded SMARTI3 project will streamline the generation of human and synthetic data for robot learning, greatly boosting and scaling up robot skill-learning capabilities. This approach combines data generation, leveraging human feedback for continuous self-improvement, fine-tuning foundational knowledge, and incorporating offline instructions and geometric information to develop key models for perception, prediction, and skill.
Objective
SMARTI3 addresses three pivotal visions in robotics: enabling robots to handle diverse objects in complex scenes, manipulate objects with varying geometries and physical properties, and interpret and follow intricate instruction manuals. While AI is predestined to realize these visions, its promises to fulfill these visions have not been fully realized yet. Despite significant breakthroughs of AI in many domains, robotics has not seen a parallel surge, primarily due to challenges in task-specific data collection, the high demand for training data, and the extensive expertise required for applying AI in robotics.
Our innovative approach integrates intuitive data generation, fine-tuning of foundational knowledge, leveraging human feedback for continual self-improvement, and incorporating offline instructions with semantic and geometric information into skill, perception, and prediction models. Our objectives are to streamline the generation of human and synthetic data for robot learning, develop few-shot adaptable foundational representations, scale-up robot skill-learning capabilities, and enhance learning from interactive human feedback and offline instructions.
The project showcases its advancements through three use cases that mirror future industrial applications: sorting and disassembling a variety of Lego pieces, origami folding from visual instructions, and assembling complex Lego structures from unsorted pieces using instruction manuals. These cases highlight our approach's adaptability and potential for industrial application.
SMARTI3 aims to revolutionize robotic skill, perception, and prediction models, significantly broadening the scope of robotic technology in various industries. This groundbreaking project lays the foundation for a new era of intelligent, adaptable, and user-friendly robotic solutions, set to rival human manipulation capabilities.
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 mathematics pure mathematics geometry
- 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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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-2024-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.
76131 Karlsruhe
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.