Project description
Innovative robot skill learning framework
Learning from demonstration (LfD) is a model for empowering robots to autonomously learn to perform new tasks. However, environmental changes, expensive demonstration costs, and potential uncertainties due to data-based learning limit its application. The EU-funded L3TD project will propose a robot skill learning framework based on the human learning process to achieve humanlike skill learning characteristics. The project will equip a teleoperated interface with multi-sensors and a special exoskeleton to minimise information difference between humans and robots, explore new theories of primitive skill (PS) learning and PS-based task graph learning, learn and generalise PS to achieve failure reasoning, and adaptation to zero and few-shot tasks.
Objective
Learning from demonstration (LfD) is a paradigm for enabling robots to autonomously learn from demos to perform new tasks. But, environmental changes, expensive demonstration cost, and potential uncertainties caused by data-based learning make it hard to be applied in actual. The project aims to propose a robot skill learning framework from human learning process via a teleoperation interface to achieve human-like skill learning characteristics such as few-shot learning, learning from failed attempts and tentative actions, and strong skill transfer and generalization ability. Five work packages will be taken to realize the objectives. First, a teleoperated interface will be equipped with multi-sensors and special exoskeleton to minimize information difference between humans and robots. After building a scalable primitive skill (PS) library based on task segmentation with multimodal information, new theories of PS learning and PS-based task graph learning are explored. PS will be learned and generalized based on improved meta-learning that is associated and explained by physical laws and neural motor disciplines. The PS-based task graph will be learned from the human learning process, achieving failure reasoning and adaptation to zero/few-shot tasks. Some practical problems e.g. incomplete data set and difference of sim-to-real applications will also be addressed. Finally, the previous theories will be certified by medical robot tasks. The applicant will acquire a solid state-of-the-art interdisciplinary scientific training in the multidisciplinary research fields, such as artificial intelligence, robotics technologies and mechanical design, and that will enable him to generate new scientific knowledge and quickly develop his research career and leadership. The final aim is to consolidate Europe as the world leader in robot and AI areas and to benefit European robotics applications in industry, surgery, and nuclear waste disposition.
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.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyother engineering and technologiesnuclear engineeringnuclear waste management
- natural sciencescomputer and information sciencesartificial intelligence
- medical and health sciencesclinical medicinesurgery
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
- social scienceslaw
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Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
BS16 1QY Bristol
United Kingdom