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Lead PI: Oliver Brock

Description of the doctoral project:

Contact-rich manipulation tasks of articulated objects, such as the opening and closing of door latches, still pose significant challenges for the state of the art in robot manipulation. This project will extract manipulation strategies from human demonstrations through kinesthetic teaching. This project will develop an innovative approach to Learning from Demonstration that learns robust policies from few, yet diverse demonstrations. The resulting data will give rise to general manipulation strategies based on techniques from machine learning. Effectively, the acquired data acts as a prior for the (deep) learning methods employed for policy generation. The goal is to produce a means of programming a robot system by merging data and learning to produce robust, complex, and general contact-rich manipulation.

Prerequisites:

  • MS degree in computer science or similar field
  • Research experience in robotics, machine learning, computer vision, and/or control
  • Experience in Learning from Demonstration and force control desirable
  • Experience in applying (deep) learning to control problems desirable
  • Interest in interdisciplinary research in the context of the Center of Excellence “Science of Intelligence”
  • Excellent software engineering and programming skills in C++
  • Excellent English writing and communication skills

 

Application deadline: 29 November 2019 23:59 CET