Robot skill learning: imitation, exploitation and control

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.