Towards Endowing Collaborative Robots with Fast Learning for Minimizing Tutors’ Demonstrations: What and When to Do?

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


Programming by demonstration allows non-experts in robot programming to train the robots in an intuitive manner. However, this learning paradigm requires multiple demonstrations of the same task, which can be time-consuming and annoying for the human tutor. To overcome this limitation, we propose a fast learning system – based on neural dynamics – that permits collaborative robots to memorize sequential information from single task demonstrations by a human-tutor. Important, the learning system allows not only to memorize long sequences of sub-goals in a task but also the time interval between them. We implement this learning system in Sawyer (a collaborative robot from Rethink Robotics) and test it in a construction task, where the robot observes several human-tutors with different preferences on the sequential order to perform the task and different behavioral time scales. After learning, memory recall (of what and when to do a sub-task) allows the robot to instruct inexperienced human workers, in a particular human-centered task scenario.


Industrial robotics Assembly tasks Learning from demonstration Sequence order and timing Rapid learning Dynamic Neural Fields 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Center for Computer GraphicsUniversity of MinhoGuimaraesPortugal
  2. 2.Department of Mathematics and Applications, Center of MathematicsUniversity of MinhoGuimaraesPortugal
  3. 3.Department Industrial ElectronicsUniversity of MinhoGuimaraesPortugal

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