1. Hierarchical learning of robotic contact policiesMihael Simonič, Aleš Ude, Bojan Nemec, 2023, izvirni znanstveni članek Povzetek: The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly.
Ključne besede: autonomous robot learning, learning, experience, compliance and impedance contro
Objavljeno v DiRROS: 21.09.2023; Ogledov: 13; Prenosov: 4
 Celotno besedilo (1,73 MB)
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6. Smart hardware integration with advanced robot programming technologies for efficient reconfiguration of robot workcellsTimotej Gašpar, Miha Deniša, Primož Radanovič, Barry Ridge, Thiusius Rajeeth Savarimuthu, Aljaž Kramberger, Marc Priggemeyer, Jürgen Rossmann, Florentin Wörgötter, Tatyana Ivanovska, Shahab Parizi, Žiga Gosar, Igor Kovač, Aleš Ude, 2020, izvirni znanstveni članek Objavljeno v DiRROS: 10.09.2020; Ogledov: 1404; Prenosov: 617
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