PhD: in Learning Methods to Enable Dynamic Human-Robot Collaborations

We are looking for a highly motivated researcher that would like to pursue a PhD in the areas of Interpretable Artificial Intelligence (IAI) and Autonomous Decision-Making to study and make significant contributions to the emerging field of Human-Robot Collaboration (HRC). HRC studies the most effective algorithms to enable robots to work safely and efficiently alongside with humans in shared spaces.

Join our multidisciplinary team to further investigate and propose innovative solutions to empower robots with the ability to make informed decisions in collaborative environments. If you are passionate about Interpretable AI, autonomous decision-making, and the future of human-robot collaboration, we strongly encourage you to apply!

Project description
Collaborative Robots (Cobots) are intended to interact directly with humans in shared and dynamic spaces. Meaning that humans and robots can work together in close proximity to achieve a common goal. What is limiting the deployment of Cobots to complex collaborative scenarios? An important research question is how to enable effective human-robot collaborations (HRC). A central challenge is to develop learning and reasoning methods to enable Cobots to autonomously make decisions, mirroring human’s ability to make quick, informed decisions in dynamic collaborative settings.

This project is addressing a major gap in the state-of-the-art in the field of Human-Robot Collaboration (HRC), which is to learn the automatic mapping between the learned mid-level representations (i.e., task operators) to low-level robotic executions (i.e. robot skills). One major problem is that these mid- and low-levels co-exist in different spaces, namely the problem space (robot/hardware agnostic) and execution space (robot/hardware dependent), respectively. Therefore, each space requires and produces different types of representations and dimensionality, making a complex non-isomorphic space mapping problem. Therefore, we aim that the Cobot learns the parameters and adapts its set of skills to execute the appropriate controllers.

Information about the division and the department
The position is within the Mechatronics and Automation groups at Chalmers University of Technology, where a team of dedicated PhD students, postdocs and senior researchers are actively pursuing research in the areas of semantic-based methods, high-level decision-making systems, Interpretable AI, and Robotics. The Mechatronics group is an integral part of the Department of Electrical Engineering (E2) which primarily focuses on modeling and developing efficient systems for extracting and processing information.

This position is financed by the Wallenberg AI, Autonomous Systems and Software Program (WASP) which is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry.

Major responsibilities
Your primary responsibility as a PhD student will be to pursue independent research and expand your capabilities and knowledge within the research domains covered by the project. You are expected to develop your own original scientific concepts and present the results of your research verbally at academic conferences and in written journal articles. You are also expected to collaborate with other team members and external researchers from academia and industry.

The PhD student will be part of the graduate school within WASP which is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/

Read more about doctoral studies at Chalmers here.

Qualifications
By the starting date, the applicant should have:

• A master's degree equivalent to at least 240 higher education credits in Electrical Engineering, Computer Sciences, Applied Math, or similar fields.
• A deep understanding and background in machine learning methods applied to the robotics domain.
• Good programming skills, preferably in C++ and experience with ROS (Robot Operating System). Additionally, knowledge on Python, Unity, and Prolog is also welcome.
• A genuine interest and curiosity in the subject, coupled with excellent oral and written English communication skills.

Meritorious experience and skills:
• Experience with teaching.
• The candidate should have experience with cross-disciplinary collaboration, as well as international exchange and demonstrated teamwork.
• It is meritorious if the candidate has peer-review publication(s) as first author.

Contract terms
This full-time position also involves specific department obligations, typically in the form of teaching, which accounts for approximately 20% of your working hours. This position is limited to a maximum of five years.

We offer
Chalmers offers a cultivating and inspiring working environment in the coastal city of Gothenburg. 
Read more about working at Chalmers and our benefits for employees.

Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example the GENIE Initiative on gender equality for excellence. Equality and diversity are substantial foundations in all activities at Chalmers.

For more information about the application procedure, please visit Chalmers website. 
Application deadline: 2024-07-22



For questions, please contact:
Assoc. Prof. Karinne Ramirez Amaro, Systems and Control,
karinne@chalmers.se