Supervisor: Prof. Ville Kyrki (email@example.com).
Advisors: Dr. Fares J. Abu-Dakka (firstname.lastname@example.org).
Keywords: Generalization, human–robot collaboration, imitation learning, orientation learning, kernelized movement primitives.
As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. To this end, we proposed an approach, based on kernelized movement primitives (KMP), that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-points and end-points), where both orientation and angular velocity are considered. The end-effector orientation is represented by a unit quaternion.
The goal of this assignment is to understand the KMP and to implement a C++ code for the kernelized treatment of orientation data in real setup.
- Review of relevant state-of-the-art literature;
- Implementation C++ code of the algorithm in real setup using Franka robotic arm;
- Evaluating the method on a physical robot.
Prerequisites: Basics of robot learning, C++, Mtlab, Linux.
Suggested tools: C++, Matlab; MuJoCo or VRep; ROS.
Platform: Franka Panda robotic arm.
Start: Available immediately
 Yanlong Huang, Fares J. Abu-Dakka and João Silvério, Darwin G. Caldwell, Towards Orientation Learning and Adaptation in Cartesian Space, IEEE Transactions on Robotics, 2020.
 Yanlong Huang, Leonel Rozo, João Silvério and Darwin G. Caldwell, Kernelized Movement Primitives, International Journal of Robotics Research, 38(7), pp.833-852, 2019.