PhD on Bimanual Robotic Manipulation in the Open World

Introduction

Do you want to work on the next generation manipulators to give robots the ability to perform bimanual pick-and-place and handling tasks Are you passionate and skilled in advanced mathematics machine learning robot dynamics robot control advanced programming and experimental validation If so you might be an excellent candidate for this PhD position. Join us helping develop a technology that could provide a step change in manufacturing logistics aviation and construction domains.

Job Description

The demand for autonomous robots capable of physically interacting with the world in flexible and adaptable ways is rapidly increasing across industries and society. These robots are needed to perform complex and fast physical interaction tasks spanning from fine manipulation such as kitting and cables manipulation to heavy non-ergonomic tasks in semi-structured environments such as depalletization and truck unloading.

This PhD project wants to explore the latest advances in machine learning physics simulation online robot control and robot hardware to generate robust contact-rich strategies that allow to perform bimanual manipulation tasks in man-made environments such as handling of parcels bags or clothes targeting industry relevant use cases at required execution speed. This is enabled by latest tactile robots that are back-drivable and capable of sensing contact interactions with the environment as well as computational hardware machine learning methods and parallel physics simulation software enabling to train complex control policies or adaptable sampling-based MPC strategies.

Key Objectives and Challenges of this PhD Position Include:

• Develop machine learning models e.g. vision-action models to manipulate parcels bags and clothes in clutter allowing to adapt to object properties on the fly while also continuously monitor task execution swiftly replanning in case of inevitable occasional failures
• Collect experimental data and make it available according to FAIR principles and where relevant use it to validate physics engines (such as e.g. Isaac Sim MuJoCo Algoryx Dynamics) against real experiments to explore the sim2real limits and use it to propose control strategies that respect and exploit the natural robot-environment contact dynamics for boosting task success rate
• Perform experimental work on the various robotic manipulation platforms available in the lab to assess progress with respect to the state of the art and showcase results to our research and industrial network

The position is embedded in the Robotics section (RBT) within the Department of the Mechanical Engineering with close connections with the Dynamics and Control (D&C) and Control Systems Technology (CST) sections in the same department as well as suitable research groups in the Mathematics and Computer Science (M&CS) department and Electrical Engineering (EE) department of the TU/e covering all expertise in machine learning computer vision knowledge representation and robot control needed for this PhD project.

Job Requirements

(please reflect explicitly on all these aspects in your application)

• A masters degree (or equivalent) in a field relevant to robot learning and control such as mechanical engineering electrical engineering control engineering computer science or related disciplines.
• Background knowledge in robot dynamics machine learning and control theory
• A research-oriented mindset eager to take on exciting challenge
• Willingness or demonstrated ability to work on multidisciplinary and collaborative projects.
• Demonstrated high-level and low-level programming skills (Python and C/C) and machine learning frameworks
• Experience with performing physical experiments on machines and/or robot manipulators
• Motivation to develop teaching skills and mentor junior students (Bachelors and Masters levels)
• Proficiency in spoken and written English (C1 level or higher).
  
  

Conditions of Employment

A meaningful job in a dynamic and ambitious university in an interdisciplinary setting and within an international network. You will work on a beautiful green campus within walking distance of the central train addition we offer you:

• Full-time employment for four years with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme paid pregnancy and maternity leave partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities scale P (min. 3059 - max. 3881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure on-campus childrens day care and sports facilities.
• Unlimited access to the modern oncampus TU/e Student Sports Center at an exceptionally affordable rate.
• An allowance for commuting working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

About us

We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact today and in the future. TU/e is home to over 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven a worldleading tech region with more than 7000 hightech companies and strong R&D activity. Known for breakthroughs in AI photonics semiconductors and advanced manufacturing Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

Information

Do you recognize yourself in this profile and would you like to know more Please contact the hiring manager Alessandro Saccon Associate Professor () for further information about the scientific content of the position.

Visit our website for more information about the application process. You can also contact HR advice or.

Curious to hear more about what its like as a PhD candidate at TU/e Please view the video.

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Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.
• Your MSc thesis report and eventually a small description of past research projects involving experimental or numerical work connected to robotic manipulation (with potential weblink to videos)
• If applicable the most recent IELTS/TOEFL/Cambridge English (or similar) exam result

Ensure that you submit all the requested application documents. We give priority to complete applications.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.