PhD position Risk-aware motion planning

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In this PhD you will investigate methods for risk-aware motion planning among decision-making agents, using learning and optimization, and apply them to self-driving cars and teams of drones.

Job description

While automation offers opportunities to make society safer, it comes with new risks, some of which are fundamental and, others more technological. Autonomous agents require an understanding of how humans respond to the uncertainty and risk that they bring. From the agent’s perspective, actions are taken to bring itself into safety if there are uncertainties, but this may introduce new risks for others. For example, a vehicle that comes to a stand- still at a strange location, or reduces speed for a green traffic light, has already resulted in accidents, simply because this does not match human expectations. It is a major challenge to develop agents and frameworks that account for uncertainty, risk and interaction in the way humans do.

As human behavior depends on a tightly controlled perception-action cycle that carefully considers uncertainty and risk, so should the behavior of an autonomous agent. But this is not trivial to attain. For example, the dynamics of the environment in which it operates can be unpredictable, and control options may be unavailable or have limited ability. Inspired by how human brains deal with uncertainty, in this project you will develop probabilistic frameworks for motion planning in autonomous agents, such as cars or teams of drones. We will work on a fundamental understanding of how autonomous agents can cope with uncertainty and provide means for computing performance guarantees of autonomous AI agents under uncertainty, which will be integrated to various degrees into a use-case with self-driving shuttles. In autonomous agents, this probabilistic framework consists of three successive stages: processing sensory information to arrive at state-estimation (what is the agent perceiving and where), determining action-plans from this state estimation (what the agent can and should do, and how much risk it should take), and converting abstract action-plans into precise motion-plans. In your PhD, you will focus on the second challenge, namely the generation of safe risk-aware motion for the autonomous robot. You will collaborate with a second PhD candidate focused on estimation.

You will be expected to test your algorithms in practice with mobile robots (ground or aerial) and a self-driving car, in collaboration with 2getthere.

For your PhD you will be embedded within the Autonomous Multi-Robots Lab in the Department of Cognitive Robotics at TU Delft.

The main focus of the Cognitive Robotics department is the development of intelligent robots and vehicles that will advance mobility, productivity and quality of life. Our mission is to bring robotic solutions to human-inhabited environments, focusing on research in the areas of machine perception, motion planning and control, machine learning, automatic control and physical interaction of intelligent machines with humans. We combine fundamental research with work on physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators and haptic interfaces. Strong collaborations exist with cross-faculty institutes TU Delft Robotics Institute and TU Delft Transport Institute, our national robotic ecosystem (RoboValley, Holland Robotics) and international industry and academia. http://www.cor.tudelft.nl/

For information about the Autonomous Multi-Robots Laboratory at the Delft University of Technology, see https://www.autonomousrobots.nl/.

Requirements

The candidate has a MSc degree in Systems and Control, Computer Science, Applied Mathematics, Robotics, or a related field. The candidate must be able to work at the intersection of several research domains and have a passion for doing ground-breaking theoretical research and applying it to real robots. Good programming skills and experience with programming languages such as Python and C++ are of foremost importance. Excellent command of the English language is required, as well as excellent communication skills. Candidates with a background in motion planning, control theory or robotics are especially encouraged to apply. Experience with reinforcement learning algorithms is a strong plus.

Starting date is flexible within the first half of 2022. Preferred start date in Q1 of 2022. Latest start date is August 2022.

Conditions of employment

TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2434 per month in the first year to € 3111 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

TU Delft (Delft University of Technology)

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact!

Faculty Mechanical, Maritime and Materials Engineering

The Faculty of 3mE carries out pioneering research, leading to new fundamental insights and challenging applications in the field of mechanical engineering. From large-scale energy storage, medical instruments, control technology and robotics to smart materials, nanoscale structures and autonomous ships. The foundations and results of this research are reflected in outstanding, contemporary education, inspiring students and PhD candidates to become socially engaged and responsible engineers and scientists. The faculty of 3mE is a dynamic and innovative faculty with an international scope and high-tech lab facilities. Research and education focus on the design, manufacture, application and modification of products, materials, processes and mechanical devices, contributing to the development and growth of a sustainable society, as well as prosperity and welfare.

Click here to go to the website of the Faculty of Mechanical, Maritime and Materials Engineering. Do you want to experience working at our faculty? This video will introduce you to some of our researchers and their work.

Additional information

For more information about this vacancy, please contact Dr. Javier Alonso-Mora, email: j.alonsomora@tudelft.nl.

For information about the procedure, please contact Hilma Bleeker, HR advisor, email: application-3mE@tudelft.nl.

Application procedure

Are you interested in this vacancy? Please apply via the application button and upload:

  1. a letter of motivation explaining why you are the right candidate for this project;
  2. an updated cv;
  3. exam transcripts from your Bachelor and Master;
  4. a copy of your MSc thesis, or draft MSc thesis (or a link to access it);
  5. email addresses of (at least) two referees;
  6. a one page (max) research statement highlighting your research skills for this position (you may comment on your past projects and what you would like to work on in the PhD).

Please submit your application as soon as possible, ultimately by December 16th, 2021. The position will be closed once a suitable candidate is found.

A pre-employment screening can be part of the selection procedure.
You can apply online. We will not process applications sent by email and/or post.

Acquisition in response to this vacancy is not appreciated.

Uren:
38 - 40
Dienstverband:
fulltime
Type vacature:
Intern

Vaardigheden

Opleiding

WO

Wat wij bieden

Contract:
Fulltime
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