PhD Candidate in Soft Aerial Robotics and Autonomy

About this job

https://youtu.be/Xt-yHCN5QS0 About the position Aerial robotics have scored significant successes yet – interestingly – they have long employed body morphologies that merely represent miniaturizations of manned aviation concepts or have resorted to biomimicry. However, novel missions and environments require that we look into new designs and concepts both for the robot’s morphology as well as its control and autonomy. To that end, soft bodies offer the possibility to create flying robots with unique capabilities. By exploiting advanced materials, we can achieve anisotropic compliance – offering softness only in the desired directions of interaction – while maintaining light weight and high control authority. Importantly, body softness can be exploited by appropriately designed control in order to achieve enhanced safety and the ability to navigate particularly cluttered and high-risk environments. In turn, deriving such control laws represents its own challenge calling for the utilization of advanced techniques including but not limited to deep reinforcement learning and model predictive control. The position is part of the Autonomous Robots Lab activities in resilient robotic autonomy across diverse operational conditions and environments. The Autonomous Robots Lab has leading research in robotic autonomy and demonstrated performance in a collection of world-class projects funded by both European and US sources. For more details about our team and our publication track record, see: https://www.autonomousrobotslab.com/publications.html. Recent examples of our work can be seen in the following videos of our robotic experimentation: https://youtu.be/l8Su8OXsM-E & https://youtu.be/nS2WgZldIv4 & https://youtu.be/OO2P4N0drGc & https://youtu.be/C6l7Vklbc9k & https://youtu.be/o56wAkelRzU For a position as a PhD Candidate, the goal is a completed doctoral education up to an obtained doctoral degree. Your immediate leader is Professor Kostas Alexis as the main scientific supervisor of the research. About the project The activity will be part of the funded Horizon Europe project “RESSORT: Resilient Efficient Soft and Sustainable Optimized Robotics Technology” in which NTNU is partner (Coordinator: VUA, Brussels). Research collaboration with the consortium partners is also expected. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within the framework described above Fundamental contributions in soft aerial robot design Fundamental contributions in control for flying robots Contributions in robot learning, model predictive control or other key method Focus on robust sim2real transfer, robot realization and testing Be prepared for changes to your work duties after employment. Required selection criteria You must have a professionally relevant background in any or all of the domains of machine learning, evolutionary algorithms, optimization, robotics and other synergistic domains. Your education must correspond to a five-year Norwegian degree program, where 120 credits are obtained at master's level. You must have a strong academic background from your previous studies and an average grade from the master's degree program, or equivalent education, which is equal to B or better compared with NTNU's grading scale. If you do not have letter grades from previous studies, you must have an equally good academic basis. If you have a weaker grade background, you may be assessed if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program (see: https://www.ntnu.edu/ie/research/phd ) PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment is to be made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria Knowledge and skills in software engineering including strong background in C++ and/or Python. Experience with robot modeling and design in CAD Experience with reinforcement learning or model predictive control. Experience with the Robot Operating System (ROS). Experience with simulators for robotic systems. Experience with implementation of real-life robotic systems. Good written and oral English skills. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Be scientifically curious and open to new research challenges. Demonstrate independence and persistence in addressing technical problems. Be flex

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