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.
CITATION STYLE
Cunha, A., Ferreira, F., Erlhagen, W., Sousa, E., Louro, L., Vicente, P., … Bicho, E. (2020). Towards Endowing Collaborative Robots with Fast Learning for Minimizing Tutors’ Demonstrations: What and When to Do? In Advances in Intelligent Systems and Computing (Vol. 1092 AISC, pp. 368–378). Springer. https://doi.org/10.1007/978-3-030-35990-4_30
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