Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/54497
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Kulvičius, Tomas;Biehl, Martin;Javad Aein, Mohamad;Tamošiūnaitė, Minija;Wörgötter, Florentin
Title: Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Is part of: Robotics and autonomous systems. Amsterdam : Elsevier Science, Vol. 61, iss. 12, 2013
Extent: p. 1450-1459
Date: 2013
Keywords: Sąveikaujantys agentai;Kliūčių vengimas;Hebinis mokymasis;Interactive agents;Obstacle avoidance;Hebbian learning
Abstract: Since several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn to cooperate. Simulations as well as real-robot experiments are shown. Interestingly, all these mechanisms are entirely based on low level interactions without any planning or cognitive component
Internet: https://doi.org/10.1016/j.robot.2013.07.009
http://www.sciencedirect.com/science/article/pii/S0921889013001358
Affiliation(s): Sistemų analizės katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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