You will be part of the Robotics Research Group at the Division of Robotics, Automation and Mechatronics, Department of Mechanical Engineering. The group has pioneered robotics research in Europe since the mid-1970s and was among the first to develop active force feedback for assembly operations. Already in 1980 it developed algorithms for learning insertions based on stochastic automata. It has covered virtually all aspects of sensor-based robotics, from the high-level task specification down to low-level sensor-based control, and applied the research results in a variety of industrial applications. In the last decade the group shifted its attention towards service robots (behaviour-based mobile manipulation, shared control, learning control), medical robotics (natural interfaces, haptic bilateral control), industrial robot assistants, and active sensing. The Department has created several spin-off companies that are active in robotics-related activities, has initiated several free and open-source software projects in robotics (Orocos, KDL, iTaSC, eTaSL, …), and has participated in a large number of EU projects in robotics, mostly oriented towards control and software development, with a focus on model-driven engineering techniques. More information is available through the link below.
The selected candidate will join the ERC Advanced Grant RobotGenSkill project. The background and approach of the project are as follows.
Future robots are expected to perform a multitude of complex tasks with high variability, in close collaboration or even physical contact with humans, and in industrial as well as in non-industrial settings. Both human-robot interaction and task variability are major challenges. A lot of progress is needed so that: (1) robots recognize the intention of the human and react with human-like motions; (2) robot end-users, such as operators on the factory floor or people at home, are able to deploy robots for new tasks or new situations in an intuitive way, for example by just demonstrating the task to the robot.
The fundamental challenge addressed in this project is: how can a robot generalize a skill that has been demonstrated in a particular situation and apply it to new situations? This project focuses on skills involving rigid objects manipulated by a robot or a human and follows a model-based approach consisting of: (1) conversion of the demonstrated data to an innovative invariant representation of motion and interaction forces; (2) generalization of this representation to a new situation by solving an optimal control problem in which similarity with the invariant representation is maintained while complying with the constraints imposed by the new context. Additional knowledge about the task can be added in the constraints.
Major breakthroughs are that the required number of demonstrations and hence the training effort decrease drastically, similarity with the demonstration is maintained in view of preserving the human-like nature, and task knowledge is easily included.
The methodology is applied to program robot skills involving motion in free space (e.g. human-robot hand over tasks) as well as advanced manipulation skills involving contact (e.g. assembly, cleaning), aiming at impact in industrial and non-industrial settings.
The RobotGenSkill team currently consists of two postdocs and three PhD researchers, supervised by two permanent staff members and the principle investigator/grant holder professor Joris De Schutter.
Your focus in the project lies on the robot control aspects during the generalization of demonstrated skills: to develop computationally tractable control schemes that allow online reactive trajectory adaptation taking into account various sensor inputs or disturbances; herein continuing/further developing the work described in https://ieeexplore.ieee.org/document/9312463 and demonstrating the developed methods in distinct applications involving motions in free space and in contact.
For a complete overview on the project and list of related publications, please check https://robotgenskill.pages.gitlab.kuleuven.be/.
A successful candidate has obtained, or is about to obtain, a PhD degree in engineering (Mechanical, Mechatronics, Mathematical, Electrical, Computer Science) related to robot programming and control, and has a strong background and interest to contribute to:
Contributions to free and open-source software projects and hands-on experience with robot platforms and sensor systems (vision, force …) are both a plus. If applicable, please list them clearly in your application or send us your portfolio.
In your motivation letter or extended CV description, please mention your previous experiences and skills, which may help to make relevant contributions to the project.
The selected candidate is furthermore expected to:
The successful candidate will receive:
A start date in the course of 2021, preferably in or before August 2021, is to be agreed upon.
Please use the online application tool to submit your application.
• an academic CV with photo
• a PDF of your diplomas and transcript of course work and grades
• statement of research interests and career goals (max. 2 pages)
• a list of publications
• contact details of at least two referees
Deadline: May 15, 2021. The position might be filled in earlier if an excellent candidate is found.
For more information please contact Prof. dr. ir. Joris De Schutter, mail: email@example.com.
You can apply for this job no later than May 15, 2021 via the online application tool
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|Título||Postdoc Position on Control of Robot Manipulators for Generalizing Human-demonstrated Skills to New Uncertain Situations|
|Job location||Oude Markt 13, 3000 Leuven|
|Publicado||febrero 19, 2021|
|Fecha límite de solicitud||mayo 15, 2021|
|Tipos de trabajo||Postdoctorado  |
|Campos||Ingeniería Mecatrónica,   Inteligencia Artificial,   Interacción Persona-Ordenador,   Ingeniería de Sistemas y de Control,   Robótica,   Ingeniería Eléctrica,   Ingeniería Mecánica,   Mecánica,   Electrónica,   |