Skip to main content
European Commission logo print header

Robot skill learning: imitation, exploitation and control

Project description

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.


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.


Net EU contribution
€ 212 933,76
Woodhouse lane
LS2 9JT Leeds
United Kingdom

See on map

Yorkshire and the Humber West Yorkshire Leeds
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00