PhD Research Scholarship in legged robot motion-planning and optimisation

    • POSTED: December 21, 2023
    • CLOSES: Open until filled
    • LOCATION: Sydney or Canberra, Australia
    • POSITION: PhD Scholarship
    • ORGANISATION: Ariam Research Hub
    • SUPERVISOR: Prof. Ian Manchester / Prof. Hanna Kurniawati
    • PhD STIPEND: $40,000 AUD (Tax-Free)

    As part of the Australian Robotic Inspection and Asset Management Research Hub (ARIAM) and in collaboration with our industry partner, Nexxis, we are currently offering two fully funded Ph.D. positions to advance the state-of-art in legged robot motion planning and optimisation. Applicants with a strong background in Mechatronic Engineering, Electrical and Computer Engineering, Computer Science, Machine Learning, or similar programs are encouraged to apply. There are two positions available at either Australian National University (ANU) or the University of Sydney.

    This project aims to develop methods and software to help endow legged robots, with reliable, robust, and efficient capabilities to perform autonomous inspection in confined, cluttered, and potentially fouled environment. This autonomous operation will allow inspection of assets and infrastructures to be conducted 24X7, which reduces the down time of these assets and infrastructures, and in turn reduces disruptions to end-users.

    Depending on interest and ability, candidates will investigate one or more of the following research questions:

    • How can integrated planning and learning methods be developed and implemented to empower a robot in preventing catastrophic failure and facilitating swift recovery from diverse failure scenarios?
    • What strategies can be devised to develop sequential, safe, and efficient scanning behaviours that enable the collection of scan data within densely cluttered and confined environments?
    • How might leveraging the additional degrees-of-freedom in a robot’s legs facilitate enhanced agility and flexibility in scanning motions? How do we exploit them for faster inspection of environment with many occluded areas, while ensuring safety for the robot operation?
    • How can an integrated approach that combines first-principles and data-driven methodologies be developed for the design of hardware, coupled with corresponding software implementations?
    • Development of automatic techniques and their software implementation for quadruped design that accounts for computational hardness of inspection planning.

    The outcomes of this project will be of considerable interest in the context of operations monitoring challenging environments.

    Research Environment

    ARIAM Hub is a collaboration between leading academic researchers and experts from the Australian robotics industry supported by the Australian Research Council to deliver research excellence in robotics for asset management. ARIAM spans 3 leading Australian universities: The University of Sydney, Queensland University of Technology (QUT) and Australian National University (ANU). These projects will be part of a collaboration between the Australian Centre for Robotics (ACFR) at the University of Sydney and the Robust Decision-Making Lab at the ANU.  You will be supervised by Professor Ian Manchester, ARIAM Hub Director and Director at ACFR or Professor Hanna Kurniawati of the Robust Decision-Making Lab within the School of Computing at ANU.

    The ACFR is the largest robotics and automation research group in Australia and is one of the largest in the world. For over 20 years the ACFR has been a leader in research and training in field robotics, with programs in agriculture, intelligent transportation systems including autonomous driving, and in marine. The ACFR offers specialised imaging labs and facilities, robotic platforms, test tanks and robotic field labs across on-campus and nearby off-campus sites. You will have access to mechanical and electronics workshops and a pool of technical staff to help realise your research ambitions. The ACFR is part of the University of Sydney which offers a rich academic setting in a world-class city.

    The School of Computing has a strong foundation in computing and information sciences at ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. Our mission is motivated by the need to design, drive and sustain strategic activities via five broad focus areas: Computing Foundations, Computational Science, Intelligent Systems, Data Science and Analytics, and the Software Innovation Institute. The PhD candidate will be part of the Intelligent Systems broad focus area, and specifically part of the Robotics group. The ANU Robust Decision-making and Learning Lab is a growing robotics group specialising in robot decision-making. Its members have developed multiple first and award-winning works in robot planning. The lab is part of the ANU Planning and Optimisation group, which is often ranked in the top three Automated Planning group in the world.

    The two roles advertised here are part of a larger opportunity that will hire and bring together a large group of PhD students and multiple Postdocs.


    Two fully funded 3.5-year PhD scholarship covering tuition fees and stipend of $40,000 (tax-free).

    About You

    Successful candidates will have:

    • A bachelor’s degree in a relevant discipline, including but not limited to  Mechatronics, Computer Science, Aeronautical Engineering, Electrical Engineering, and Mechanical Engineering.
    • Interest in robotics research
    • Excellent communication and interpersonal skills
    • Creativity, curiosity, and passion
    • Experience with one or more of computer vision, decision-making, machine learning, planning, control
    • Have a good understanding of abstract data structures and basic algorithms
    • Fluent in programming with high-level language, such as C++ / Python
    • Hands-on experience with robotic platforms, ROS, robotic simulator (e.g., Gazebo, CoppeliaSim), and/or deep learning frameworks would also be an asset

    How to Apply

    To apply, please use the application form below and include the following:

    • CV
    • Unofficial transcripts
    • Cover letter
    • Your preference to attend either University of Sydney or Australian National University

    Optional but appreciated – Please also include a Google Drive link to a 2-minute selfie video covering the following:

    • Your strongest engineering skills
    • What do you enjoy most about developing technology
    • A description of a project that you’re proud of, or plan to be when completed

    International Applicants

    Domestic and international applicants are welcome. The Australian PhD is a 3.5-year program, generally with direct entry from an undergraduate degree with a final-year thesis project (see Admission Criteria below).

    Candidates complete a total of two graduate-level classes of their choice as part of the PhD program. There are no doctoral qualifying / candidacy exams. Candidates complete a viva / oral thesis defence at the completion of the program.

    Admission Criteria

    Successful candidates will need to enrol in the University’s Doctor of Philosophy (Engineering) program. Enrolment requirements are listed on the University website:

    University of Sydney

    Australian National University 

    Key requirements are:

    • An Undergraduate or Master’s degree with overall first-class Honours or equivalent, AND
    • Some sort of research experience, either:
      • Completion of an Undergraduate degree with a final-year thesis/project, OR
      • Completion of a Master’s by research degree, OR
      • Completion of a Master’s by coursework degree with a substantial research project.


    Apply Now

    Please add your highest achieved grade in the grading format of your university.
    Please add the academic institutions that you have attended.
    Drop files here or
    Max. file size: 20 MB.
      Please upload your CV, cover letter and transcripts. Please include a link to a Google Drive link to a 2-minute selfie video as per the requirements listed above.