PhD position on predictive control and estimation in sensor-based robot tasks

18 February 2020

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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)

- sample of technical writing (publication or thesis)

- contact details of at least two referees

Deadline: April 30, 2020. Note: the position might be filled in earlier if an excellent candidate is found.

For more information, send an e-mail to

You can apply for this job no later than April 30, 2020 via the online application tool

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Apply before 30 April 2020

You will be part of the Robotics Research Group at the 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 learning insertion algorithms 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.

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Robots are a cornerstone of industrial automation, and their importance is expected to further increase in the future. At the same time, the environment in which robots operate is dramatically changing: instead of executing a single task in very structured and fixed production lines, more and more companies need a very flexible deployment of robots. Instead of being locked up in their cage, with work pieces going in and out in a strictly defined way, robots are now being deployed in environments closer to human co-workers, with the position of work pieces much less accurately defined. Hence, robots need to perform multiple and more complex tasks, involving extensive sensing such as vision and force sensing.

Deploying robots for such tasks is challenging for three reasons: (i) sensing and estimation become an indispensable part of the control loop, as the robot initially has little information about the environment; (ii) the control itself becomes more challenging because both the complexity of the tasks and the required performance increase; (iii) the effort required for (re)programming the robot for its different tasks should be kept low, since the programming effort is a major cost factor and plays a large role in the economic viability of a robot application.

This project addresses the above challenges by using advanced control and estimation algorithms that are made available to the application programmers using a simple task specification language.

In previous work we have developed the iTaSC [1,2] and subsequently eTaSL/eTC [3] frameworks and task specification language for this purpose. Using this task specification language, an application developer can specify sensor-based control tasks without needing extensive knowledge about robot control strategies. The current implementation of eTaSL/eTC [4] uses a velocity-resolved strategy to transform a task specification into a controller that uses a QP-solver. The current control strategy is instantaneous in nature and hence, purely reactive: the controller does not proactively plan its future control actions ahead of time and is, consequently, severely limited in its ability to meet the multitude of requirements intrinsic to robot manipulation: fast and accurate task execution while guaranteeing safety, and respecting the robot’s position, velocity and torque limits as well as constraints imposed by the environment.

Model predictive control (MPC) is a natural paradigm for rendering the controller proactive, and central in this PhD project: MPC solves at every time instant an optimal control problem to compute optimal future control actions given a model of the system and its environment. Its optimisation-based nature supplies MPC with unique abilities to incorporate complete system dynamics, to directly handle system constraints, and to efficiently adapt to changes in the environment. On the other hand, for robot manipulators, MPC demands the solution of a highly nonlinear and non-convex optimisation problem at every time sample. This contrasts sharply with the simple QPs involved in the instantaneous velocity-resolved control approaches of iTaSC and eTaSL/eTC.

Important research targets in this PhD project are (1) strategies for mitigation of the high computational complexity of MPC. Seminal work by researchers has turned to tailored implementations for particular tasks, considering very short prediction horizons, and/or adopting particular linearization of the robot kinematics and dynamics; and (2) proper inclusion of robot dynamics; “abstracting” dynamics and considering a robot purely on a (task space or joint) motion level severely limits the potential.

[1] De Schutter J., De Laet T., Rutgeerts J., Decré W., Smits R., Aertbeliën E., Claes K., Bruyninckx H. (2007). Constraint-based task specification and estimation for sensor-based robot systems in the presence of geometric uncertainty. International Journal of Robotics Research, 26 (5), 433-455.

[2] Decré W., De Schutter J., Bruyninckx H. (2013). Extending the Itasc Constraint-Based Robot Task Specification Framework to Time-Independent Trajectories and User-Configurable Task Horizons. In: Robotics and Automation (ICRA), 2013 IEEE International Conference on (1941-1948). Presented at the ICRA, ISBN: 978-1-4673-5643-5.

[3] Aertbeliën E., De Schutter J. (2014). Etasl/eTC: A Constraint-Based Task Specification Language and Robot Controller Using Expression Graphs. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (1540-1546). Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, 14 Sep 2014-18 Sep 2014.

[4] eTaSL:


A successful candidate has obtained a MSc degree in engineering (Mechanical, Mechatronics, Mathematical, Electrical, Computer Science) related to Robotics and has a strong background and interest to contribute to:

  • numerical optimization, control theory
  • real-time control, embedded control systems, software engineering for robotics

Contributions to free and open source software projects (also beyond the topic of the project!) 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 consider to mention your previous experiences and skills, which may help to make relevant contributions to the project.

The selected candidate is furthermore expected to:

  • have a very good knowledge of English (spoken and written)
  • be able to work independently, accurately and methodically
  • be a team player
  • present research findings at national and international conferences
  • publish research findings in international journals

The succesful candidate will receive:

  • a fully funded doctoral scholarship for one year, renewable up to four years
  • multiple benefits (health insurance, access to university infrastructure and sports facilities, etc)
  • the opportunity to participate in research collaborations and international conferences

A start date in the course of 2020, preferably in or before September 2020, is to be agreed upon.