Programming and re-programming robots is extremely time-consuming and expensive, which presents a major bottleneck for new industrial, agricultural, care, and household robot applications. The goal of this project is to enable robots to learn how to perform manipulation tasks from few human demonstrations, based on novel interactive machine learning techniques. Robot learning will no longer rely on initial demonstrations only, but it will effectively use additional user feedback to continuously optimize the task performance. It will enable the user to directly perceive and correct undesirable behavior and to quickly guide the robot toward the target behavior. The PhD will explore various aspects of interactive imitation learning: robot-driven disambiguation, the robot actively testing possibilities, as well as user-driven corrections. The approaches will be evaluated with generic real-world robotic force-interaction tasks related to handling and (dis)assembly. The potential of the newly developed teaching framework will be demonstrated with challenging bi-manual tasks and a final study evaluating how well novice human operators can teach novel tasks to a robot.
The PhD position is in the context of the project “Teaching Robots Interactively” (TERI), funded by the European Research Council as ERC Starting Grant.
The candidate has a very good MSc degree in systems and control, mechanical engineering, applied mathematics, artificial intelligence, machine learning, electrical engineering, computer science, or a related field. The candidate must have strong analytical skills and must be able to work at the intersection of several research domains. A very good command of the English language is required, as well as excellent communication skills. Candidates having exhibited their ability to perform research in robotics and/or machine learning are especially encouraged to apply.
If you have specific questions about this position, please contact Dr. Jens Kober, e-mail: [hidden email] . Please do not send application emails here, but use the specified address below.
To apply, please prepare:
- a letter of motivation explaining why you are the right candidate,
- a detailed CV,
- a complete record of Bachelor and Master courses (including grades),
- your Master’s Thesis (at least as draft),
- any publications, and a list of projects you have worked on with brief descriptions of your contributions (max 2 pages), and
- the names and contact addresses of two or three references.
All these items should be combined in one PDF document. Applications should be submitted by email at the earliest convenience to [hidden email] . When applying for this position, please refer to vacancy number 3mE18-68. The call for applications will remain open until the ideal candidate is found. The earliest starting date is 1 February 2019.