Project description DEENESFRITPL New and improved imitation learning framework With the increasing demand for robotics in industrial and many other sectors worldwide, innovations in the field of robotics are much sought after. However, the field of robotics currently faces many challenges, ranging from design to skill learning; overcoming these challenges is imperative for progress to be achieved in this field. The EU-funded RobotSL project will provide an improved and optimised imitation learning methodology for robots to improve skill learning. This methodology will allow for more adaptable skills enabling robots to work in several different sectors and complete a more varied set of tasks. Show the project objective Hide the project objective Objective In this project, I will develop an imitation learning framework for robot skill learning and optimization, aiming at endowing robots with versatile skills and thus allowing robots to work in broad application domains. This framework will handle various constraints (e.g. robot joint limit, trajectory smoothness, obstacle avoidance) that robots encounter in practice, exploit environmental priors and multi-modal properties underlying human demonstrations, as well as design a low-level optimal controller so as to drive robots to execute human-like motions and resist external perturbations. The project objectives and associated concepts are original and novel. This project will provide the first solution for the problem of imitation learning with various constraints (including linear and non-linear, convex and non-convex constraints) and a novel concept of semi-imitation learning by exploring environmental priors. Moreover, it will provide a solution to multi-modal imitation learning from few demonstrations, which can be readily combined with constrained learning and environmental priors. In addition, from a control perspective, this project will study a new concept of control-inspired imitation learning to mimic both human skills and human reactions under perturbations. This project is challenging in the sense that it involves robotics, imitation learning, probability theory, optimization, semi-supervised learning, clustering techniques and optimal control. I will work closely with Prof. Cohn, who is an expert in knowledge representation and reasoning. This fellowship will sharpen my research skills and extend my research network in Leeds and Europe. Specifically, this fellowship will enable me to dive deeper into the challenging but essential problems in robot imitation learning, which will provide new insights and research topics to the community of robot learning, positioning me as a competitive researcher in the community. Fields of science natural sciencescomputer and information sciencesknowledge engineeringnatural sciencesmathematicsapplied mathematicsstatistics and probabilityengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2020 - Individual Fellowships Call for proposal H2020-MSCA-IF-2020 See other projects for this call Funding Scheme MSCA-IF-EF-ST - Standard EF Coordinator UNIVERSITY OF LEEDS Net EU contribution € 212 933,76 Address Woodhouse lane LS2 9JT Leeds United Kingdom See on map Region Yorkshire and the Humber West Yorkshire Leeds Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
