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Project 1.1: Cobot contact tasks through multi-sensory deep learning

Principal Supervisor

Project based at

QUT

Lead Partner Organisation

Weld Australia

Cobot contact tasks through multi-sensory deep learning

This project will take a new approach that detects and diagnoses the dynamical process through deep learning fusion of multi-sensory data, including force/tactile, visual, thermal, sound, and acoustic emission; and generate corrective process parameters in achieving the goals of a contact task. The Project will investigate and develop new theories and methods for machine and process modeling, and model-based robot contact control with intrinsic safety in time-and space-variant processes

The Australian Cobotics Centre is looking for a highly motivated PhD student to join this interdisciplinary collaborative project based at QUT. The PhD student will work on new theory and methods for multi-sensory deep learning and model-based robot contact control in time-and space-variant processes.

Skills and Experience

Desired Background

  • Excellent communication skills
  • Robotics, computer science, or mechatronics background
  • Strong background in mathematics

Desired Skills

  • Programing in C++, Python or Matlab
  • Knowledge of Robotics Operating Systems (ROS)

Associated Researchers

Jonathan Roberts

Centre Director
Queensland University of Technology
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XiaoQi Chen

Research Program Co-lead (Biomimic Cobots program)
Swinburne University of Technology
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Teresa Vidal-Calleja

Research Program Co-lead (Biomimic Cobots program)
University of Technology Sydney
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Mats Isaksson

Chief Investigator
Swinburne University of Technology
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