PostDoc in Multi-agent Reinforcement Learning for Robotic Construction

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 16,000 people, including over 12,000 students and 4,000 researchers from more than 120 different countries.

Your mission :

The EPFL SYCAMORE laboratory lead by Prof. Maryam Kamgarpour and EPFL CRCL lead by Prof. Stefana Parascho are looking to hire a postdoctoral researcher at the intersection of reinforcement learning and multi-robot construction. You will collaborate with both of our labs on an innovative project, to design, verify and evaluate multi-agent reinforcement learning algorithms for multi-robot assembly tasks.

This position is funded by the Center for Intelligent Systems at EPFL.

EPFL launched the Center for Intelligent Systems (EPFL CIS) in October 2019. This center is a joint initiative of the schools ENAC, IC, SB, STI and SV that integrates research in Artificial Intelligence (AI), Machine Learning (ML) and Robotics to foster integrative research streams leading to the development of Intelligent Systems. Intelligent Systems perceive their environment, can learn from the data they collect and adapt to the changing world around them. Intelligent Systems can take many forms. When available, such systems will have profound implications for many areas including manufacturing, transportation, commerce, employment, healthcare, government, legal, security, privacy, and education.
Four integrative research pillars have already been launched since the inception of CIS: “AI for Medicine”, “Intelligent Assistive Robotics”, “Decentralized edge AI Infrastructure” and “Digital Twin”.
Furthermore, the EPFL Center for Intelligent Systems supports its 75 associated professors in their teaching and training mission of future engineers/scientists.
In addition, the EPFL CIS serves as a “one-stop-shop” for questions from industry, society and politics on the topics of artificial intelligence, machine learning and robotics and pro-actively promotes technology transfer from research to society and industry by organizing thematic conferences such as the EPFL Digital Twin Days in November 2021 or thematic events such as the AI for engineering roundtable during the EPFL engineering industry day 2023.
Finally, through its work and contacts, the CIS integrates EPFL into European and international research and excellence networks.

The project aims at increasing robots’ impact on a sustainable environment, by expanding their autonomy. Multi-robotic assembly applications have shown great potential for the efficient construction of structures in controlled environments but have yet to be employed in uncontrolled ones. The approach we pursue in increasing robot autonomy in construction is based on the development of multi-agent reinforcement learning theory and algorithms for autonomous multi-robot assembly and construction and their transfer to the physical world. Reinforcement learning has a great potential to find solutions to the highly complex problems of multi-robot assembly: sequencing, path-planning, task-allocation. However, the assembly application brings additional theoretical and algorithmic challenges for multi-agent reinforcement learning. These challenges include very sparse rewards capturing task completion, heterogenous state information of each robot due to a given robot using local sensors, and a very large state-space due to all possible configurations for the assembly task. In addition, design considerations are key to achieving not only a functioning construction process, but one that exceeds human design and construction capabilities. The outcome of the postdoc will result in provably convergent and efficient multiagent RL algorithms and their implementation and evaluation on a multi-robotic testbed, in collaboration with doctoral students of both labs.

Main duties and responsibilities include :
Perform original research at the intersection of reinforcement learning and multi-robot assembly

Your profile :
Applicants should have completed or be close to completing a PhD in reinforcement learning, with a focus on application of algorithms in robotics. A strong scientific background and a proven publication record in the above fields is required. Experience in construction robotics is a plus, as well as a documented interest in design. Excellent communication skills in English are
expected.

We offer :

- An opportunity and support to develop a scientific career in an exciting area of research

- A stimulating educational environment in two talented, young and motivated EPFL      research group

- The opportunity to work at the intersection of Robotics, Control, Engineering,

Computer Science [SP1]

and Design in a unique interdisciplinary setting

- Excellent living conditions and competitive renumeration.

We value diversity and strongly encourage applications from individuals from all identities and backgrounds. We are committed to providing a supportive work environment and increasing diversity by supporting your individual needs at every step of your career. If you’re motivated, want to be part of a unique, multicultural, collaborative, and inclusive community, and help shape the future – then consider this opportunity.

Please apply with the following documents :

- Cover letter (at most 1 page)

- CV (including list of publications)

- Transcripts

- Portfolio (if applicable)

- Contact information for two willing reference letter writers

- Two representative publications

Contact: [email protected]
[email protected]

Start date :
As early as possible.

Term of employment :
Fixed-term (CDD)

Duration :
Duration: 1 year

Remark :
Only candidates who applied through EPFL website or our partner Jobup’s website will be considered. Files sent by agencies without a mandate will not be taken into account.

Reference :
Job Nb 2894

apply online